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1914
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1929
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1930
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1931
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1932
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1933
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1934
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1935
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1936
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1937
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1939
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1940
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1941
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1942
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1943
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1944
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1945
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1950
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1953
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1954
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1956
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1958
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1959
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1980
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1981
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1982
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1983
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1984
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1986
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1987
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1988
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1989
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1990
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2005
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2007
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2015
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3200
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3201
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3214
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3215
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3216
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3217
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3218
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3219
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3220
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3221
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3222
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3224
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3225
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3226
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3227
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3228
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3229
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3230
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3231
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3232
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3233
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3235
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3236
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3237
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3238
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3239
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3240
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3241
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3242
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3243
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3244
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3245
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3246
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3247
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3248
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3249
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3252
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3254
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3255
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3256
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3257
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3259
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3260
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3261
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3262
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3263
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3264
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3265
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3266
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3267
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3268
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3269
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3270
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3271
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3272
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3276
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3277
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3279
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3280
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3281
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3284
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3285
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3286
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3287
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3289
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3290
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3291
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3292
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3293
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3295
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3296
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3297
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3298
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3299
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3300
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3301
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3302
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3303
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3304
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3305
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3306
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3307
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3311
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3312
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3313
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3329
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3330
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3331
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3332
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1945
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1946
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1948
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1949
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1950
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1951
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1952
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1953
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1954
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1955
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1962
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1963
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1965
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1966
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1967
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1968
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1969
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1970
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1971
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1972
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1973
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1974
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1975
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1976
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1977
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1978
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1979
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1980
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1981
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1982
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1983
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1984
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1985
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1987
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1988
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1989
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1990
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1991
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1992
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1993
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1994
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1995
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1996
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1997
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1998
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1999
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2000
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2001
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2002
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2010
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2011
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2012
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2013
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2014
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2015
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2016
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2017
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2018
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2019
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2020
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2021
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2281
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2282
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2283
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2284
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2285
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2286
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2287
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2288
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2289
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2290
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2291
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2292
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2293
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2294
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2295
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2296
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2297
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2298
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2299
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2300
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2301
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2302
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2303
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2304
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2305
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2306
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2307
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2308
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2309
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2310
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2311
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2312
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2313
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2314
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2315
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2316
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2317
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2318
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2319
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2320
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2321
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2322
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2323
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2324
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2325
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2326
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2327
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2328
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2329
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2330
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2331
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2332
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2333
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2334
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2335
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2336
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2337
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2338
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2339
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2340
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2341
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2342
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2343
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2344
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2345
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2346
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2347
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2348
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2349
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2350
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2351
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2352
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2353
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2354
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2355
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2356
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2357
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2358
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2359
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2360
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2361
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2362
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2363
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2364
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2365
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2366
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2367
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2368
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2369
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2370
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2371
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2372
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2373
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2374
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2375
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2376
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2377
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2378
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2379
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2380
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2381
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2382
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2383
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2384
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2385
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2386
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2387
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2388
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2389
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2390
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2391
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2392
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2393
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2394
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2395
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2396
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2397
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2398
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2399
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2400
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2401
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2402
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2403
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2404
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2405
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2406
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2407
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2408
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2409
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2410
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2411
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2412
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2413
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2414
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2415
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2416
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2417
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2418
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2419
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2420
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2421
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2422
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2423
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2424
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2425
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2426
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2427
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2428
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2429
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2430
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2431
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2432
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2433
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2434
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2435
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2436
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2437
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2438
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2439
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2440
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2441
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2442
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2443
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2444
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2445
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2446
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2447
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2448
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2449
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2450
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2451
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2452
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2453
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2454
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2455
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2456
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2457
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2458
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2459
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2460
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2461
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2462
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2463
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2464
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2465
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2466
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2467
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2468
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2469
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2470
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2471
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2472
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2473
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2474
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2475
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2476
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2477
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2478
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2479
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2480
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2481
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2482
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2483
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2484
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2485
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2486
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2487
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2488
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2489
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2490
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2491
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2492
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2493
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2494
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2495
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2496
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2497
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2498
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2499
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2500
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2501
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2502
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2503
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2504
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2505
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2506
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2507
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2508
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2509
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2510
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2511
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2512
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2513
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2514
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2515
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2516
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2517
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2518
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2519
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2520
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2521
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2522
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2523
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2524
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2525
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2526
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2527
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2528
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2529
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2530
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2531
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2532
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2533
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2534
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2535
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2536
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2537
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2538
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2539
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2540
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2541
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2542
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2543
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2544
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2545
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2546
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2547
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2548
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2549
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2550
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2551
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2552
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2553
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2554
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2555
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2556
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2557
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2558
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2559
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2560
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2561
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2562
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2563
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2564
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2565
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2566
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2567
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2568
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2569
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2570
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2571
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2572
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2573
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2574
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2575
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2576
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2577
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2578
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2579
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2580
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2581
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2582
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2583
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2584
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2585
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2586
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2587
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2588
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2589
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2590
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2591
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2592
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2593
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2594
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2595
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2596
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2597
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2598
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2599
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2600
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2601
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2602
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2603
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2604
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2605
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2606
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2607
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2608
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2609
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2610
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2611
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2612
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2613
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2614
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2615
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2616
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2617
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2618
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2619
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2620
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2621
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2622
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2623
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2624
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2625
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2626
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2627
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2628
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2629
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2630
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2631
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2632
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2633
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2634
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2635
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2636
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2637
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2638
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2639
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2640
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2641
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2642
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2643
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2644
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2645
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2646
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2647
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2648
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2649
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2650
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2651
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2652
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2653
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2654
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2655
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2656
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2657
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2658
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2659
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2660
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2661
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2662
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2663
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2664
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2665
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2666
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2667
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2668
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2669
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2670
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2671
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2672
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2673
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2674
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2675
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2676
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2677
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2678
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2679
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2680
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2681
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2682
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2683
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2684
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2685
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2686
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2687
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2688
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2689
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2690
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2691
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2692
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2693
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2694
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2695
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2696
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2697
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2698
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2699
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2700
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2701
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2702
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2703
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2704
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2705
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2706
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2707
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2708
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2709
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2710
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2711
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2712
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2713
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2714
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2715
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2716
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2717
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2718
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2719
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2720
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2721
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2722
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2723
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2724
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2725
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2726
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2727
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2728
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2729
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2730
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2731
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2732
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2733
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2734
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2735
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2736
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2737
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2738
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2739
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2740
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2741
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2742
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2743
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2744
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2745
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2746
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2747
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2748
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2749
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2750
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2751
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2752
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2753
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2754
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2755
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2756
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2757
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2758
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2759
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2760
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2761
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2762
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2763
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2764
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2765
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2766
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app.py ADDED
@@ -0,0 +1,1114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import re
3
+ import io
4
+ import base64
5
+ from dataclasses import dataclass
6
+ from typing import Dict, Any, List, Optional, Tuple
7
+ import requests
8
+ import gradio as gr
9
+ import matplotlib.pyplot as plt
10
+ import numpy as np
11
+ import pandas as pd
12
+ import seaborn as sns
13
+ import shap
14
+ import statsmodels.api as sm
15
+ from openai import OpenAI
16
+ from sklearn.metrics import mean_absolute_percentage_error, mean_squared_error
17
+ from sklearn.model_selection import train_test_split
18
+ from statsmodels.tsa.seasonal import seasonal_decompose
19
+ from statsmodels.tsa.statespace.sarimax import SARIMAX
20
+ from statsmodels.tsa.stattools import adfuller
21
+ from xgboost import XGBRegressor
22
+ from datetime import timedelta
23
+
24
+
25
+ ART = None
26
+
27
+ # ============================================================
28
+ # CONFIG
29
+ # ============================================================
30
+ PATH_EQUIPOS = "historico_equipos.csv"
31
+ PATH_X = "X.csv"
32
+ PATH_Y = "Y.csv"
33
+ PATH_Z = "Z.csv"
34
+
35
+ ROLL_WINDOWS = [3, 6, 9, 12, 15, 30]
36
+ LAGS = [1, 3, 6, 12, 15, 30]
37
+ SARIMA_ORDER = (1, 1, 1)
38
+ SARIMA_SEASONAL_ORDER = (1, 1, 1, 12)
39
+ DEFAULT_FORECAST_HORIZON = 30
40
+ XGB_RANDOM_STATE = 42
41
+
42
+
43
+ # ============================================================
44
+ # DATA CLASSES
45
+ # ============================================================
46
+ @dataclass
47
+ class TrainingArtifacts:
48
+ df_raw: pd.DataFrame
49
+ df_model: pd.DataFrame
50
+ features: List[str]
51
+ model1: XGBRegressor
52
+ model2: XGBRegressor
53
+ xgb_metrics: Dict[str, float]
54
+ arima_metrics: Dict[str, float]
55
+ adf_results: Dict[str, float]
56
+ ols_full_summary: str
57
+ ols_reduced_summary: str
58
+ shap_model1: pd.DataFrame
59
+ shap_model2: pd.DataFrame
60
+ xgb_importance_model1: pd.DataFrame
61
+ xgb_importance_model2: pd.DataFrame
62
+ top_corr: pd.DataFrame
63
+ seasonal_comment: str
64
+ sarimax_x_summary: str
65
+ sarimax_z_summary: str
66
+
67
+
68
+ # ============================================================
69
+ # LOAD + CLEAN
70
+ # ============================================================
71
+ def load_data(
72
+ path_equipos: str = PATH_EQUIPOS,
73
+ path_x: str = PATH_X,
74
+ path_y: str = PATH_Y,
75
+ path_z: str = PATH_Z,
76
+ ) -> pd.DataFrame:
77
+ equipos = pd.read_csv(path_equipos)
78
+ x_df = pd.read_csv(path_x)
79
+ y_df = pd.read_csv(path_y, sep=";")
80
+ z_df = pd.read_csv(path_z)
81
+
82
+ equipos["Date"] = pd.to_datetime(equipos["Date"], format="%Y-%m-%d")
83
+ x_df["Date"] = pd.to_datetime(x_df["Date"], format="%Y-%m-%d")
84
+ y_df["Date"] = pd.to_datetime(y_df["Date"], format="%d/%m/%Y")
85
+ z_df["Date"] = pd.to_datetime(z_df["Date"], format="%Y-%m-%d")
86
+ y_df["Price"] = y_df["Price"].astype(str).str.replace(",", ".", regex=False).astype(float)
87
+
88
+ # Se toma el dataset consolidado de equipos como base del modelamiento final
89
+ df = equipos.copy().sort_values("Date").reset_index(drop=True)
90
+ return df
91
+
92
+
93
+ # ============================================================
94
+ # FEATURE ENGINEERING
95
+ # ============================================================
96
+ def create_features(df_base: pd.DataFrame) -> pd.DataFrame:
97
+ df = df_base.copy().sort_values("Date").reset_index(drop=True)
98
+
99
+ for w in ROLL_WINDOWS:
100
+ df[f"X_prom_{w}"] = df["Price_X"].rolling(window=w).mean()
101
+ df[f"Z_prom_{w}"] = df["Price_Z"].rolling(window=w).mean()
102
+
103
+ for lag in LAGS:
104
+ df[f"X_lag_{lag}"] = df["Price_X"].shift(lag)
105
+ df[f"Z_lag_{lag}"] = df["Price_Z"].shift(lag)
106
+
107
+ df["X_diff"] = df["Price_X"].diff()
108
+ df["Z_diff"] = df["Price_Z"].diff()
109
+ return df
110
+
111
+
112
+ # ============================================================
113
+ # TESTS + EDA
114
+ # ============================================================
115
+ def adf_pvalue(series: pd.Series) -> float:
116
+ return float(adfuller(series.dropna())[1])
117
+
118
+
119
+ def compute_ols_summaries(df: pd.DataFrame) -> Tuple[str, str]:
120
+ x_full = sm.add_constant(df[["Price_X", "Price_Y", "Price_Z"]])
121
+ y = df["Price_Equipo2"]
122
+ model_full = sm.OLS(y, x_full).fit()
123
+
124
+ x_reduced = sm.add_constant(df[["Price_X", "Price_Z"]])
125
+ model_reduced = sm.OLS(y, x_reduced).fit()
126
+ return model_full.summary().as_text(), model_reduced.summary().as_text()
127
+
128
+
129
+ def seasonal_commentary(df: pd.DataFrame) -> str:
130
+ comment = (
131
+ "Se evalu贸 estacionalidad mediante inspecci贸n visual y descomposici贸n aditiva sobre Price_Z. "
132
+ "Como las materias primas muestran estructura temporal y no estacionariedad, se seleccion贸 SARIMA "
133
+ "para capturar tendencia, diferenciaci贸n y componente estacional."
134
+ )
135
+ try:
136
+ _ = seasonal_decompose(df.set_index("Date")["Price_Z"], model="additive", period=12)
137
+ except Exception:
138
+ comment += " No obstante, la descomposici贸n puede ser sensible a ventanas y valores faltantes."
139
+ return comment
140
+
141
+
142
+ def compute_top_correlations(df: pd.DataFrame) -> pd.DataFrame:
143
+ cols = ["Price_X", "Price_Y", "Price_Z", "Price_Equipo1", "Price_Equipo2"]
144
+ corr = df[cols].corr().round(4)
145
+ return corr
146
+
147
+
148
+ # ============================================================
149
+ # SARIMA
150
+ # ============================================================
151
+ def fit_sarima(series: pd.Series) -> Any:
152
+ model = SARIMAX(series, order=SARIMA_ORDER, seasonal_order=SARIMA_SEASONAL_ORDER)
153
+ return model.fit(disp=False)
154
+
155
+
156
+ def sarima_forecast_with_ci(df: pd.DataFrame, steps: int) -> Tuple[pd.DataFrame, Any, Any]:
157
+ df_sorted = df.sort_values("Date").reset_index(drop=True)
158
+ x_model = fit_sarima(df_sorted["Price_X"])
159
+ z_model = fit_sarima(df_sorted["Price_Z"])
160
+
161
+ x_fcst = x_model.get_forecast(steps=steps)
162
+ z_fcst = z_model.get_forecast(steps=steps)
163
+
164
+ future_dates = pd.date_range(start=df_sorted["Date"].max(), periods=steps + 1, freq="D")[1:]
165
+ x_ci = x_fcst.conf_int().reset_index(drop=True)
166
+ z_ci = z_fcst.conf_int().reset_index(drop=True)
167
+
168
+ out = pd.DataFrame(
169
+ {
170
+ "Date": future_dates,
171
+ "Price_X": x_fcst.predicted_mean.reset_index(drop=True),
172
+ "Price_Z": z_fcst.predicted_mean.reset_index(drop=True),
173
+ "X_lower": x_ci.iloc[:, 0],
174
+ "X_upper": x_ci.iloc[:, 1],
175
+ "Z_lower": z_ci.iloc[:, 0],
176
+ "Z_upper": z_ci.iloc[:, 1],
177
+ }
178
+ )
179
+ return out, x_model, z_model
180
+
181
+
182
+ def backtest_sarima_price_x(df: pd.DataFrame, steps: int = 30) -> Dict[str, float]:
183
+ # Replica la l贸gica de tu notebook: comparaci贸n con la cola real de X fuera de equipos
184
+ x_df = pd.read_csv(PATH_X)
185
+ x_df["Date"] = pd.to_datetime(x_df["Date"], format="%Y-%m-%d")
186
+ x_df = x_df.sort_values("Date").reset_index(drop=True)
187
+
188
+ fitted = fit_sarima(df.sort_values("Date")["Price_X"])
189
+ fcst = fitted.get_forecast(steps=steps).predicted_mean.reset_index(drop=True)
190
+ real = x_df[x_df["Date"] >= df["Date"].max()].head(steps).reset_index(drop=True)
191
+ common = min(len(real), len(fcst))
192
+ if common == 0:
193
+ return {"mse_x": np.nan, "mape_x": np.nan}
194
+
195
+ y_true = real["Price"].iloc[:common]
196
+ y_pred = fcst.iloc[:common]
197
+ return {
198
+ "mse_x": float(mean_squared_error(y_true, y_pred)),
199
+ "mape_x": float(mean_absolute_percentage_error(y_true, y_pred)),
200
+ }
201
+
202
+
203
+ # ============================================================
204
+ # XGBOOST + SHAP
205
+ # ============================================================
206
+ def build_model_dataset(df: pd.DataFrame) -> Tuple[pd.DataFrame, List[str]]:
207
+ df_feat = create_features(df)
208
+ drop_cols = ["Date", "Price_Y", "Price_Equipo1", "Price_Equipo2"]
209
+ features = [c for c in df_feat.columns if c not in drop_cols]
210
+ df_model = df_feat.dropna().reset_index(drop=True)
211
+ return df_model, features
212
+
213
+
214
+ def train_xgb_models(df: pd.DataFrame) -> Tuple[pd.DataFrame, List[str], XGBRegressor, XGBRegressor, Dict[str, float], pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
215
+ df_model, features = build_model_dataset(df)
216
+ x = df_model[features]
217
+ y1 = df_model["Price_Equipo1"]
218
+ y2 = df_model["Price_Equipo2"]
219
+
220
+ x_train, x_test, y1_train, y1_test = train_test_split(x, y1, test_size=0.2, shuffle=False)
221
+ _, _, y2_train, y2_test = train_test_split(x, y2, test_size=0.2, shuffle=False)
222
+
223
+ model1 = XGBRegressor(random_state=XGB_RANDOM_STATE)
224
+ model2 = XGBRegressor(random_state=XGB_RANDOM_STATE)
225
+ model1.fit(x_train, y1_train)
226
+ model2.fit(x_train, y2_train)
227
+
228
+ y1_pred = model1.predict(x_test)
229
+ y2_pred = model2.predict(x_test)
230
+
231
+ metrics = {
232
+ "rmse_equipo1": float(np.sqrt(mean_squared_error(y1_test, y1_pred))),
233
+ "mape_equipo1": float(mean_absolute_percentage_error(y1_test, y1_pred)),
234
+ "rmse_equipo2": float(np.sqrt(mean_squared_error(y2_test, y2_pred))),
235
+ "mape_equipo2": float(mean_absolute_percentage_error(y2_test, y2_pred)),
236
+ }
237
+
238
+ imp1 = pd.DataFrame({"Feature": features, "Importance": model1.feature_importances_}).sort_values("Importance", ascending=False)
239
+ imp2 = pd.DataFrame({"Feature": features, "Importance": model2.feature_importances_}).sort_values("Importance", ascending=False)
240
+
241
+ shap1 = shap.Explainer(model1, x_train)(x_test)
242
+ shap2 = shap.Explainer(model2, x_train)(x_test)
243
+ shap1_df = pd.DataFrame({
244
+ "Feature": x_test.columns,
245
+ "Importance": np.abs(shap1.values).mean(axis=0)
246
+ }).sort_values("Importance", ascending=False)
247
+ shap2_df = pd.DataFrame({
248
+ "Feature": x_test.columns,
249
+ "Importance": np.abs(shap2.values).mean(axis=0)
250
+ }).sort_values("Importance", ascending=False)
251
+
252
+ return df_model, features, model1, model2, metrics, imp1, imp2, shap1_df, shap2_df
253
+
254
+
255
+ # ============================================================
256
+ # FULL TRAINING ARTIFACTS
257
+ # ============================================================
258
+ def build_training_artifacts() -> TrainingArtifacts:
259
+ df = load_data()
260
+
261
+
262
+ ols_full, ols_reduced = compute_ols_summaries(df)
263
+ adf_results = {
264
+ "Price_X": adf_pvalue(df["Price_X"]),
265
+ "Price_Z": adf_pvalue(df["Price_Z"]),
266
+ "Price_X_diff": adf_pvalue(df["Price_X"].diff()),
267
+ "Price_Z_diff": adf_pvalue(df["Price_Z"].diff()),
268
+
269
+ }
270
+ arima_metrics = backtest_sarima_price_x(df, steps=30)
271
+
272
+ df_model, features, model1, model2, xgb_metrics, imp1, imp2, shap1, shap2 = train_xgb_models(df)
273
+
274
+ # Reentrenamiento con 100% de los datos modelables
275
+ full_x = df_model[features]
276
+ full_y1 = df_model["Price_Equipo1"]
277
+ full_y2 = df_model["Price_Equipo2"]
278
+ model1.fit(full_x, full_y1)
279
+ model2.fit(full_x, full_y2)
280
+ sarimax_x = fit_sarima(df["Price_X"])
281
+ sarimax_z = fit_sarima(df["Price_Z"])
282
+
283
+ return TrainingArtifacts(
284
+ df_raw=df,
285
+ df_model=df_model,
286
+ features=features,
287
+ model1=model1,
288
+ model2=model2,
289
+ xgb_metrics=xgb_metrics,
290
+ arima_metrics=arima_metrics,
291
+ adf_results=adf_results,
292
+ ols_full_summary=ols_full,
293
+ ols_reduced_summary=ols_reduced,
294
+ shap_model1=shap1,
295
+ shap_model2=shap2,
296
+ xgb_importance_model1=imp1,
297
+ xgb_importance_model2=imp2,
298
+ top_corr=compute_top_correlations(df),
299
+ seasonal_comment=seasonal_commentary(df),
300
+ sarimax_x_summary=sarimax_x.summary().as_text(),
301
+ sarimax_z_summary=sarimax_z.summary().as_text(),
302
+
303
+ )
304
+
305
+
306
+ # ============================================================
307
+ # FORECASTS: MEAN / LOWER / UPPER FOR EQUIPOS
308
+ # ============================================================
309
+ def _build_future_features_from_sarima(base_df: pd.DataFrame, forecast_df: pd.DataFrame) -> pd.DataFrame:
310
+ hist = base_df[["Date", "Price_X", "Price_Z"]].copy()
311
+ future = forecast_df[["Date", "Price_X", "Price_Z"]].copy()
312
+ full = pd.concat([hist, future], axis=0, ignore_index=True)
313
+ full = create_features(full)
314
+ last_hist_date = base_df["Date"].max()
315
+ future_feat = full[full["Date"] > last_hist_date].copy().reset_index(drop=True)
316
+ return future_feat
317
+
318
+
319
+ def forecast_targets(artifacts: TrainingArtifacts, steps: int = DEFAULT_FORECAST_HORIZON) -> Dict[str, pd.DataFrame]:
320
+ base_df = artifacts.df_raw.copy()
321
+ fcst_mean, _, _ = sarima_forecast_with_ci(base_df, steps=steps)
322
+
323
+ fcst_lower = fcst_mean.copy()
324
+ fcst_upper = fcst_mean.copy()
325
+ fcst_lower["Price_X"] = fcst_mean["X_lower"]
326
+ fcst_lower["Price_Z"] = fcst_mean["Z_lower"]
327
+ fcst_upper["Price_X"] = fcst_mean["X_upper"]
328
+ fcst_upper["Price_Z"] = fcst_mean["Z_upper"]
329
+
330
+ mean_feat = _build_future_features_from_sarima(base_df, fcst_mean)
331
+ lower_feat = _build_future_features_from_sarima(base_df, fcst_lower)
332
+ upper_feat = _build_future_features_from_sarima(base_df, fcst_upper)
333
+
334
+ x_mean = mean_feat[artifacts.features]
335
+ x_lower = lower_feat[artifacts.features]
336
+ x_upper = upper_feat[artifacts.features]
337
+
338
+ pred_mean = pd.DataFrame({
339
+ "Date": mean_feat["Date"],
340
+ "Price_Equipo1_pred": artifacts.model1.predict(x_mean),
341
+ "Price_Equipo2_pred": artifacts.model2.predict(x_mean),
342
+ })
343
+ pred_lower = pd.DataFrame({
344
+ "Date": lower_feat["Date"],
345
+ "Equipo1_lower": artifacts.model1.predict(x_lower),
346
+ "Equipo2_lower": artifacts.model2.predict(x_lower),
347
+ })
348
+ pred_upper = pd.DataFrame({
349
+ "Date": upper_feat["Date"],
350
+ "Equipo1_upper": artifacts.model1.predict(x_upper),
351
+ "Equipo2_upper": artifacts.model2.predict(x_upper),
352
+ })
353
+
354
+ pred_final = pred_mean.merge(pred_lower, on="Date").merge(pred_upper, on="Date")
355
+
356
+ # suavizado ligero de bandas para visualizaci贸n
357
+ for col in ["Equipo1_lower", "Equipo1_upper", "Equipo2_lower", "Equipo2_upper"]:
358
+ pred_final[f"{col}_smooth"] = pred_final[col].rolling(3, min_periods=1).mean()
359
+
360
+ return {
361
+ "x_z_forecast": fcst_mean,
362
+ "targets_forecast": pred_final,
363
+ }
364
+
365
+
366
+ # ============================================================
367
+ # PLOTS
368
+ # ============================================================
369
+ def _fig_to_array(fig: plt.Figure):
370
+ return fig
371
+
372
+
373
+ def plot_xz_forecast(artifacts: TrainingArtifacts, steps: int = 30, commodity: str = "X") -> plt.Figure:
374
+ fcst = forecast_targets(artifacts, steps=steps)["x_z_forecast"]
375
+ df = artifacts.df_raw.copy()
376
+
377
+ fig, ax = plt.subplots(figsize=(12, 5))
378
+ if commodity.upper() == "X":
379
+ ax.plot(df["Date"].iloc[-60:], df["Price_X"].iloc[-60:], label="Real X", linewidth=2)
380
+ ax.plot(fcst["Date"], fcst["Price_X"], linestyle="--", label="Forecast X", linewidth=2)
381
+ ax.fill_between(fcst["Date"], fcst["X_lower"], fcst["X_upper"], alpha=0.2, label="Intervalo X")
382
+ ax.set_title(f"Price X: pron贸stico a {steps} d铆as")
383
+ else:
384
+ ax.plot(df["Date"].iloc[-60:], df["Price_Z"].iloc[-60:], label="Real Z", linewidth=2)
385
+ ax.plot(fcst["Date"], fcst["Price_Z"], linestyle="--", label="Forecast Z", linewidth=2)
386
+ ax.fill_between(fcst["Date"], fcst["Z_lower"], fcst["Z_upper"], alpha=0.2, label="Intervalo Z")
387
+ ax.set_title(f"Price Z: pron贸stico a {steps} d铆as")
388
+ ax.axvline(x=df["Date"].max(), linestyle=":", label="Inicio forecast")
389
+ ax.grid(True)
390
+ ax.legend()
391
+ plt.tight_layout()
392
+ return fig
393
+
394
+
395
+ def plot_target_forecast(artifacts: TrainingArtifacts, steps: int = 30, target: str = "Equipo2") -> plt.Figure:
396
+ outputs = forecast_targets(artifacts, steps=steps)
397
+ pred = outputs["targets_forecast"]
398
+ df = artifacts.df_raw.copy()
399
+
400
+ fig, ax = plt.subplots(figsize=(12, 5))
401
+ if target.lower() == "equipo1":
402
+ ax.plot(df["Date"].iloc[-60:], df["Price_Equipo1"].iloc[-60:], label="Real Equipo 1", linewidth=2)
403
+ ax.plot(pred["Date"], pred["Price_Equipo1_pred"], linestyle="--", linewidth=2, label="Pron贸stico Equipo 1")
404
+ ax.fill_between(pred["Date"], pred["Equipo1_lower_smooth"], pred["Equipo1_upper_smooth"], alpha=0.15, label="Intervalo")
405
+ ax.set_title(f"Equipo 1: pron贸stico a {steps} d铆as")
406
+ else:
407
+ ax.plot(df["Date"].iloc[-60:], df["Price_Equipo2"].iloc[-60:], label="Real Equipo 2", linewidth=2)
408
+ ax.plot(pred["Date"], pred["Price_Equipo2_pred"], linestyle="--", linewidth=2, label="Pron贸stico Equipo 2")
409
+ ax.fill_between(pred["Date"], pred["Equipo2_lower_smooth"], pred["Equipo2_upper_smooth"], alpha=0.15, label="Intervalo")
410
+ ax.set_title(f"Equipo 2: pron贸stico a {steps} d铆as")
411
+ ax.axvline(x=df["Date"].max(), linestyle=":", label="Inicio forecast")
412
+ ax.grid(True)
413
+ ax.legend()
414
+ plt.tight_layout()
415
+ return fig
416
+
417
+
418
+ # ============================================================
419
+ # KNOWLEDGE BASE FOR THE AGENT
420
+ # ============================================================
421
+ def methodology_text(artifacts: TrainingArtifacts) -> str:
422
+ return f"""
423
+ **Metodolog铆a utilizada**
424
+
425
+ 1. **Datos de entrada**
426
+ - Se us贸 como base el dataset consolidado `historico_equipos`, que ya contiene `Price_X`, `Price_Y`, `Price_Z`, `Price_Equipo1` y `Price_Equipo2`.
427
+ - Tambi茅n se revisaron las fuentes individuales de X, Y y Z para validar consistencia temporal.
428
+
429
+ 2. **EDA**
430
+ - Se revisaron rangos de fechas, tipos de dato, calidad de fechas y precios.
431
+ - Se graficaron las series de equipos y materias primas.
432
+ - Se construy贸 matriz de correlaci贸n entre X, Y, Z y ambos equipos.
433
+
434
+ 3. **Selecci贸n de variables**
435
+ - `Price_Y` se elimin贸 del modelamiento final porque, aunque muestra correlaci贸n con Equipo 2, en regresi贸n m煤ltiple comparte informaci贸n con Z.
436
+ - Se compar贸 una OLS con X, Y, Z vs otra con X, Z. La versi贸n reducida mantiene interpretaci贸n m谩s estable y evita redundancia.
437
+
438
+ 4. **Justificaci贸n de SARIMA**
439
+ - Se aplic贸 Dickey-Fuller aumentado a X y Z. Los p-values iniciales fueron mayores a 0.05, indicando no estacionariedad.
440
+ - Tras diferenciar, la serie se estabiliza mejor.
441
+ - Adem谩s se inspeccion贸 estacionalidad y por eso se eligi贸 SARIMA en lugar de ARIMA simple.
442
+
443
+ 5. **Modelo final de targets**
444
+ - Se generaron features temporales: promedios m贸viles y lags de X y Z.
445
+ - Con esas variables se entrenaron dos modelos XGBoost: uno para Equipo 1 y otro para Equipo 2.
446
+ - Se evalu贸 SHAP para interpretar importancia de variables.
447
+
448
+ 6. **Pron贸stico**
449
+ - Primero se pronostican X y Z con SARIMA.
450
+ - Luego se proyectan Equipo 1 y Equipo 2 usando XGBoost sobre las features futuras.
451
+ - Se construyen escenarios mean, lower y upper propagando la incertidumbre de X y Z.
452
+ """.strip()
453
+
454
+
455
+ def build_answer_catalog(artifacts: TrainingArtifacts, steps: int) -> Dict[str, str]:
456
+ xgb = artifacts.xgb_metrics
457
+ arima = artifacts.arima_metrics
458
+ adf = artifacts.adf_results
459
+
460
+ return {
461
+ "datos": (
462
+ f"Se trabaj贸 con un dataset consolidado que contiene Date, Price_X, Price_Y, Price_Z, Price_Equipo1 y Price_Equipo2. "
463
+ f"El rango temporal del dataset base va de {artifacts.df_raw['Date'].min().date()} a {artifacts.df_raw['Date'].max().date()}."
464
+ ),
465
+ "eda": (
466
+ "En el EDA se revisaron fechas, consistencia de variables, series hist贸ricas, correlaci贸n entre materias primas y equipos, "
467
+ "y comportamiento temporal de X, Y y Z frente a Price_Equipo1 y Price_Equipo2."
468
+ ),
469
+ "variables_eliminadas": (
470
+ "La variable eliminada del modelamiento final fue Price_Y. Se conserv贸 X y Z como drivers principales y se a帽adieron lags y promedios m贸viles de ambas."
471
+ ),
472
+ "por_que_y": (
473
+ "Price_Y se retir贸 porque en regresi贸n m煤ltiple con X, Y y Z parte de su efecto estaba absorbido por Z. "
474
+ "Eso sugiere colinealidad y redundancia. Por estabilidad e interpretabilidad se prioriz贸 el modelo con X y Z."
475
+ ),
476
+ "sarima": (
477
+ f"Se us贸 SARIMA porque X y Z no eran estacionarias al inicio. Los p-values ADF fueron: X={adf['Price_X']:.4f}, Z={adf['Price_Z']:.4f}. "
478
+ f"Tras diferenciar, mejoran a X_diff={adf['Price_X_diff']:.4f}, Z_diff={adf['Price_Z_diff']:.4f}. {artifacts.seasonal_comment}"
479
+ ),
480
+ "kpis_arima": (
481
+ f"Backtest del modelo de X con horizonte 30: MSE={arima['mse_x']:.4f}, MAPE={arima['mape_x']:.2%}."
482
+ ),
483
+ "shap": (
484
+ "SHAP se us贸 para medir la contribuci贸n de cada feature a las predicciones de XGBoost. "
485
+ "Las variables temporales derivadas de X y Z tienden a dominar, lo que indica que el modelo aprende no solo del nivel actual de los insumos sino de su din谩mica reciente."
486
+ ),
487
+ "xgb": (
488
+ f"Resultados del modelo XGBoost en holdout temporal: Equipo 1 -> RMSE={xgb['rmse_equipo1']:.4f}, MAPE={xgb['mape_equipo1']:.2%}; "
489
+ f"Equipo 2 -> RMSE={xgb['rmse_equipo2']:.4f}, MAPE={xgb['mape_equipo2']:.2%}."
490
+ ),
491
+ "forecast": (
492
+ f"El agente puede proyectar Equipo 1 y Equipo 2 a {steps} d铆as o cualquier horizonte solicitado. Adem谩s construye escenarios central, lower y upper."
493
+ ),
494
+ "metodologia": methodology_text(artifacts),
495
+ "materias_primas": (
496
+ f"Se pronosticaron Price_X y Price_Z con SARIMAX usando "
497
+ f"SARIMA_ORDER={SARIMA_ORDER} y SARIMA_SEASONAL_ORDER={SARIMA_SEASONAL_ORDER}. "
498
+ "Se incluyen escenarios mean, lower y upper, adem谩s de la justificaci贸n metodol贸gica y resultados del ajuste."
499
+ ),
500
+ "correlacion": (
501
+ "Se evalu贸 la relaci贸n entre Price_X, Price_Y, Price_Z, Price_Equipo1 y Price_Equipo2 mediante una matriz de correlaci贸n. "
502
+ "Esto permite identificar asociaciones lineales relevantes, posibles redundancias y relaciones fuertes entre materias primas y targets."
503
+ ),
504
+ }
505
+
506
+ def interpret_results(question: str, artifacts, forecast_outputs=None) -> str:
507
+ xgb = artifacts.xgb_metrics
508
+ arima = artifacts.arima_metrics
509
+ adf = artifacts.adf_results
510
+
511
+ partes = []
512
+
513
+ # Interpretaci贸n XGBoost
514
+ partes.append(
515
+ f"En el modelo final XGBoost, el desempe帽o del Equipo 1 fue RMSE={xgb['rmse_equipo1']:.2f} y MAPE={xgb['mape_equipo1']:.2%}, "
516
+ f"mientras que para el Equipo 2 fue RMSE={xgb['rmse_equipo2']:.2f} y MAPE={xgb['mape_equipo2']:.2%}."
517
+ )
518
+
519
+ if xgb["mape_equipo2"] < xgb["mape_equipo1"]:
520
+ partes.append(
521
+ "Esto indica que el modelo predice mejor el Equipo 2 que el Equipo 1, probablemente porque la relaci贸n entre las variables explicativas y el precio del Equipo 2 es m谩s estable o m谩s f谩cil de capturar."
522
+ )
523
+ else:
524
+ partes.append(
525
+ "Esto indica que el modelo predice mejor el Equipo 1 que el Equipo 2, lo cual sugiere una estructura de se帽al m谩s consistente para ese target."
526
+ )
527
+
528
+ # Interpretaci贸n ARIMA/SARIMA
529
+ partes.append(
530
+ f"En el caso de la materia prima X, el modelo SARIMA obtuvo MSE={arima['mse_x']:.2f} y MAPE={arima['mape_x']:.2%}."
531
+ )
532
+
533
+ if arima["mape_x"] < 0.05:
534
+ partes.append(
535
+ "Ese MAPE es excelente, por lo que el pron贸stico de X puede considerarse confiable como insumo del modelo final."
536
+ )
537
+ elif arima["mape_x"] < 0.15:
538
+ partes.append(
539
+ "Ese MAPE es bueno y sugiere un desempe帽o adecuado para el horizonte evaluado."
540
+ )
541
+ else:
542
+ partes.append(
543
+ "Ese MAPE refleja un error relevante, por lo que conviene usar el forecast con cautela."
544
+ )
545
+
546
+ # ADF
547
+ partes.append(
548
+ f"Las pruebas ADF iniciales muestran no estacionariedad en X (p-value={adf['Price_X']:.4f}) y Z (p-value={adf['Price_Z']:.4f}), "
549
+ f"mientras que tras diferenciar las series los p-values bajan a X_diff={adf['Price_X_diff']:.4f} y Z_diff={adf['Price_Z_diff']:.4f}."
550
+ )
551
+ partes.append(
552
+ "Eso justifica el uso de diferenciaci贸n y respalda la elecci贸n de SARIMA para modelar la din谩mica temporal."
553
+ )
554
+
555
+ # Si hay forecast, a帽adir lectura del horizonte
556
+ if forecast_outputs is not None:
557
+ pred = forecast_outputs["targets_forecast"]
558
+
559
+ eq1_ini = pred["Price_Equipo1_pred"].iloc[0]
560
+ eq1_fin = pred["Price_Equipo1_pred"].iloc[-1]
561
+ eq2_ini = pred["Price_Equipo2_pred"].iloc[0]
562
+ eq2_fin = pred["Price_Equipo2_pred"].iloc[-1]
563
+
564
+ dir_eq1 = "al alza" if eq1_fin > eq1_ini else "a la baja"
565
+ dir_eq2 = "al alza" if eq2_fin > eq2_ini else "a la baja"
566
+
567
+ partes.append(
568
+ f"En el horizonte proyectado, el Equipo 1 muestra una trayectoria {dir_eq1}, pasando de {eq1_ini:.2f} a {eq1_fin:.2f}, "
569
+ f"mientras que el Equipo 2 muestra una trayectoria {dir_eq2}, pasando de {eq2_ini:.2f} a {eq2_fin:.2f}."
570
+ )
571
+
572
+ amp_eq1 = (pred["Equipo1_upper"].iloc[-1] - pred["Equipo1_lower"].iloc[-1])
573
+ amp_eq2 = (pred["Equipo2_upper"].iloc[-1] - pred["Equipo2_lower"].iloc[-1])
574
+
575
+ if amp_eq2 > amp_eq1:
576
+ partes.append(
577
+ "Adem谩s, el intervalo del Equipo 2 es m谩s amplio al final del horizonte, lo que indica mayor incertidumbre acumulada en su forecast."
578
+ )
579
+ else:
580
+ partes.append(
581
+ "Adem谩s, el intervalo del Equipo 1 resulta m谩s amplio al final del horizonte, lo que indica mayor sensibilidad o incertidumbre acumulada en ese forecast."
582
+ )
583
+
584
+ partes.append(
585
+ "En conclusi贸n, la soluci贸n h铆brida SARIMA + XGBoost es consistente con la estructura del problema: SARIMA captura la din谩mica temporal de los insumos y XGBoost transforma esa informaci贸n en predicciones finales m谩s flexibles para los equipos."
586
+ )
587
+
588
+ return "\n\n".join(partes)
589
+
590
+ # ============================================================
591
+ # OPTIONAL LLM LAYER
592
+ # ============================================================
593
+ def llm_analyze(
594
+ question: str,
595
+ artifacts: TrainingArtifacts,
596
+ key: str,
597
+ base_answer: str,
598
+ table: Optional[pd.DataFrame] = None,
599
+ ) -> str:
600
+ api_key = os.getenv("OPENAI_API_KEY")
601
+ print("OPENAI_API_KEY presente:", bool(api_key))
602
+ print("Pregunta:", question)
603
+ print("Key detectada:", key)
604
+
605
+ market_context = {
606
+ "news_price_x": get_market_news("commodity prices OR industrial input prices OR metals market"),
607
+ "news_price_z": get_market_news("construction materials prices OR commodity z market"),
608
+ "fred_cpi": get_fred_series("CPIAUCSL", limit=3), # inflaci贸n USA
609
+ "fred_dxy": get_fred_series("DTWEXBGS", limit=3), # broad dollar index
610
+ "fred_rate": get_fred_series("FEDFUNDS", limit=3), # tasas
611
+ }
612
+
613
+ if not api_key:
614
+ return "[DEBUG] OPENAI_API_KEY no encontrada. El agente est谩 respondiendo sin LLM.\n\n" + base_answer
615
+
616
+ try:
617
+ client = OpenAI(api_key=api_key)
618
+ external_context = build_external_context(artifacts, 30)
619
+ contexto = {
620
+ "xgb_metrics": artifacts.xgb_metrics,
621
+ "arima_metrics": artifacts.arima_metrics,
622
+ "adf_results": artifacts.adf_results,
623
+ "top_corr": artifacts.top_corr.to_dict(),
624
+ "top_shap_model1": artifacts.shap_model1.head(10).to_dict(orient="records"),
625
+ "top_shap_model2": artifacts.shap_model2.head(10).to_dict(orient="records"),
626
+ "top_xgb_model1": artifacts.xgb_importance_model1.head(10).to_dict(orient="records"),
627
+ "top_xgb_model2": artifacts.xgb_importance_model2.head(10).to_dict(orient="records"),
628
+ "seasonal_comment": artifacts.seasonal_comment,
629
+ "contexto_mercado": market_context,
630
+ "external_context": external_context
631
+ }
632
+
633
+ if table is not None and not table.empty:
634
+ contexto["table_preview"] = table.head(15).to_dict(orient="records")
635
+
636
+ prompt = f"""
637
+ Pregunta del usuario:
638
+ {question}
639
+
640
+ Tipo de consulta detectado:
641
+ {key}
642
+
643
+ Respuesta base t茅cnica:
644
+ {base_answer}
645
+
646
+ #Resultados disponibles del an谩lisis:
647
+ #{contexto}
648
+
649
+ Instrucciones:
650
+ - Responde como un analista de datos senior.
651
+ - Analiza, compara y concluye.
652
+ - Si hay m茅tricas, interpr茅talas.
653
+ - Si hay forecast, describe tendencia, riesgo e incertidumbre.
654
+ - Si hay SHAP, explica qu茅 variables dominan y qu茅 implica eso.
655
+ - Si hay ADF/SARIMA, explica por qu茅 la metodolog铆a es consistente.
656
+ - Si hay contexto de mercado, relaci贸nalo con las variaciones de X y Z.
657
+ - Usa noticias y variables macro solo para complementar, no para inventar causalidad.
658
+ - Explica si el contexto externo refuerza o contradice el forecast.
659
+ - No inventes datos.
660
+ - No repitas solo el paso a paso; da conclusi贸n.
661
+ - S茅 claro y profesional.
662
+ - Relaciona el comportamiento proyectado con el contexto macroecon贸mico de Colombia
663
+ - Usa las noticias como evidencia reciente del mercado
664
+ - NO asumas que las noticias ocurren en el futuro
665
+ - Explica si el forecast es consistente con inflaci贸n, tasas o contexto econ贸mico
666
+ - Identifica riesgos (inflaci贸n, tasas, energ铆a, tipo de cambio)
667
+ - Da conclusiones claras y ejecutivas
668
+ NO repitas el paso a paso t茅cnico.
669
+ NO inventes datos.
670
+ """
671
+
672
+ resp = client.responses.create(
673
+ model="gpt-5.4-mini",
674
+ input=[
675
+ {
676
+ "role": "system",
677
+ "content": (
678
+ "Eres un analista senior de forecasting y machine learning. "
679
+ "Tu tarea es interpretar resultados reales, no repetir plantillas. "
680
+ "Debes razonar sobre m茅tricas, incertidumbre, drivers y conclusiones."
681
+ ),
682
+ },
683
+ {
684
+ "role": "user",
685
+ "content": prompt,
686
+ },
687
+ ],
688
+ )
689
+ print("LLM respondi贸 correctamente")
690
+ return resp.output_text.strip()
691
+
692
+ except Exception as e:
693
+ return f"{base_answer}\n\n[No se pudo generar an谩lisis LLM: {str(e)}]"
694
+
695
+
696
+ # ============================================================
697
+ # ROUTER
698
+ # ============================================================
699
+ def extract_horizon(text: str, default: int = DEFAULT_FORECAST_HORIZON) -> int:
700
+ nums = re.findall(r"\d+", text)
701
+ return int(nums[0]) if nums else default
702
+
703
+
704
+ def route_question(question: str) -> str:
705
+ q = question.lower()
706
+
707
+ if any(k in q for k in ["dato", "entrada", "input"]):
708
+ return "datos"
709
+
710
+ if "eda" in q or "explor" in q:
711
+ return "eda"
712
+
713
+ if "elimin" in q and "variable" in q:
714
+ return "variables_eliminadas"
715
+
716
+ if "price_y" in q or ("y" in q and "elim" in q):
717
+ return "por_que_y"
718
+
719
+ if "sarima" in q or "dickey" in q or "adf" in q or "estacion" in q or "estacional" in q:
720
+ return "kpis_arima" if "kpi" in q else "sarima"
721
+
722
+ if "shap" in q:
723
+ return "shap"
724
+
725
+ if "xgb" in q or "xgboost" in q or "modelo final" in q:
726
+ return "xgb"
727
+
728
+ if "metod" in q or "como se hizo" in q or "qu茅 hicimos" in q or "que hicimos" in q:
729
+ return "metodologia"
730
+ if any(k in q for k in [
731
+ "materia prima", "materias primas", "price_x", "price_z",
732
+ "commodities", "commodity", "insumos", "x y z", "price x", "price z"
733
+ ]):
734
+ return "materias_primas"
735
+
736
+ if any(k in q for k in ["correlacion", "correlaci贸n", "heatmap", "matriz de correlacion", "matriz de correlaci贸n"]):
737
+ return "correlacion"
738
+
739
+ if any(k in q for k in [
740
+ "analiza", "analisis", "an谩lisis",
741
+ "conclusion", "conclusi贸n",
742
+ "interpreta", "interpretacion", "interpretaci贸n",
743
+ "grafica", "gr谩fica", "graficas", "gr谩ficas",
744
+ "grafico", "gr谩fico", "graficos", "gr谩ficos",
745
+ "pronost", "forecast", "proyecc",
746
+ "equipo 1", "equipo 2"
747
+ ]):
748
+ return "forecast"
749
+
750
+ return "metodologia"
751
+
752
+ ## Materias Primas
753
+ def plot_commodities_forecast(artifacts: TrainingArtifacts, steps: int = 30) -> plt.Figure:
754
+ fcst = forecast_targets(artifacts, steps=steps)["x_z_forecast"]
755
+ df = artifacts.df_raw.copy()
756
+
757
+ fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True)
758
+
759
+ axes[0].plot(df["Date"].iloc[-60:], df["Price_X"].iloc[-60:], label="Real X", linewidth=2)
760
+ axes[0].plot(fcst["Date"], fcst["Price_X"], linestyle="--", label="Forecast X", linewidth=2)
761
+ axes[0].fill_between(fcst["Date"], fcst["X_lower"], fcst["X_upper"], alpha=0.2, label="Intervalo X")
762
+ axes[0].axvline(x=df["Date"].max(), linestyle=":", label="Inicio forecast")
763
+ axes[0].set_title(f"Price X: pron贸stico a {steps} d铆as")
764
+ axes[0].grid(True)
765
+ axes[0].legend()
766
+
767
+ axes[1].plot(df["Date"].iloc[-60:], df["Price_Z"].iloc[-60:], label="Real Z", linewidth=2)
768
+ axes[1].plot(fcst["Date"], fcst["Price_Z"], linestyle="--", label="Forecast Z", linewidth=2)
769
+ axes[1].fill_between(fcst["Date"], fcst["Z_lower"], fcst["Z_upper"], alpha=0.2, label="Intervalo Z")
770
+ axes[1].axvline(x=df["Date"].max(), linestyle=":", label="Inicio forecast")
771
+ axes[1].set_title(f"Price Z: pron贸stico a {steps} d铆as")
772
+ axes[1].grid(True)
773
+ axes[1].legend()
774
+
775
+ plt.tight_layout()
776
+ return fig
777
+ ### Graficas Shaps
778
+ def plot_shap_bar(shap_df: pd.DataFrame, title: str) -> plt.Figure:
779
+ top = shap_df.head(10).sort_values("Importance", ascending=True)
780
+
781
+ fig, ax = plt.subplots(figsize=(10, 5))
782
+ ax.barh(top["Feature"], top["Importance"])
783
+ ax.set_title(title)
784
+ ax.grid(True, axis="x", alpha=0.3)
785
+ plt.tight_layout()
786
+ return fig
787
+
788
+ ##Correlacion
789
+ def plot_correlation_heatmap(corr_df: pd.DataFrame) -> plt.Figure:
790
+ fig, ax = plt.subplots(figsize=(8, 6))
791
+ sns.heatmap(corr_df, annot=True, cmap="coolwarm", ax=ax)
792
+ ax.set_title("Matriz de correlaci贸n")
793
+ plt.tight_layout()
794
+ return fig
795
+
796
+ ### News
797
+
798
+ def get_market_news(query: str, page_size: int = 5) -> List[Dict[str, str]]:
799
+ api_key = os.getenv("NEWS_API_KEY")
800
+ if not api_key:
801
+ return []
802
+
803
+ url = "https://newsapi.org/v2/everything"
804
+ params = {
805
+ "q": query,
806
+ "pageSize": page_size,
807
+ "sortBy": "publishedAt",
808
+ "language": "en",
809
+ "apiKey": api_key,
810
+ }
811
+
812
+ try:
813
+ r = requests.get(url, params=params, timeout=20)
814
+ r.raise_for_status()
815
+ data = r.json()
816
+ return [
817
+ {
818
+ "title": a.get("title", ""),
819
+ "source": a.get("source", {}).get("name", ""),
820
+ "publishedAt": a.get("publishedAt", ""),
821
+ "description": a.get("description", ""),
822
+ "url": a.get("url", ""),
823
+ }
824
+ for a in data.get("articles", [])[:page_size]
825
+ ]
826
+ except Exception:
827
+ return []
828
+ #### News Colombia
829
+ def get_colombia_news(last_date, lookback_days=30):
830
+
831
+ api_key = os.getenv("NEWS_API_KEY")
832
+ if not api_key:
833
+ return []
834
+
835
+ from_date = (last_date - timedelta(days=lookback_days)).strftime("%Y-%m-%d")
836
+ to_date = last_date.strftime("%Y-%m-%d")
837
+
838
+ url = "https://newsapi.org/v2/everything"
839
+
840
+ params = {
841
+ "q": "(Colombia OR colombiano) AND (inflacion OR tasas OR construccion OR energia OR commodities)",
842
+ "from": from_date,
843
+ "to": to_date,
844
+ "sortBy": "publishedAt",
845
+ "language": "es",
846
+ "pageSize": 5,
847
+ "apiKey": api_key,
848
+ }
849
+
850
+ try:
851
+ r = requests.get(url, params=params, timeout=20)
852
+ r.raise_for_status()
853
+ data = r.json()
854
+
855
+ return [
856
+ {
857
+ "title": a.get("title"),
858
+ "source": a.get("source", {}).get("name"),
859
+ "date": a.get("publishedAt"),
860
+ "desc": a.get("description"),
861
+ }
862
+ for a in data.get("articles", [])
863
+ ]
864
+
865
+ except Exception:
866
+ return []
867
+
868
+
869
+
870
+ ##Macro
871
+ def get_fred_series(series_id: str, limit: int = 5) -> List[Dict[str, str]]:
872
+ api_key = os.getenv("FRED_API_KEY")
873
+ if not api_key:
874
+ return []
875
+
876
+ url = "https://api.stlouisfed.org/fred/series/observations"
877
+ params = {
878
+ "series_id": series_id,
879
+ "api_key": api_key,
880
+ "file_type": "json",
881
+ "sort_order": "desc",
882
+ "limit": limit,
883
+ }
884
+
885
+ try:
886
+ r = requests.get(url, params=params, timeout=20)
887
+ r.raise_for_status()
888
+ data = r.json()
889
+ return data.get("observations", [])
890
+ except Exception:
891
+ return []
892
+
893
+ ###Macro Colombia
894
+
895
+ def get_colombia_macro():
896
+
897
+ api_key = os.getenv("FRED_API_KEY")
898
+ if not api_key:
899
+ return {}
900
+
901
+ url = "https://api.stlouisfed.org/fred/series/observations"
902
+
903
+ def fetch(series_id):
904
+ try:
905
+ r = requests.get(url, params={
906
+ "series_id": series_id,
907
+ "api_key": api_key,
908
+ "file_type": "json",
909
+ "sort_order": "desc",
910
+ "limit": 12
911
+ }, timeout=20)
912
+
913
+ return r.json().get("observations", [])
914
+ except:
915
+ return []
916
+
917
+ return {
918
+ "inflacion": fetch("FPCPITOTLZGCOL"),
919
+ "cpi": fetch("COLCPALTT01IXNBM"),
920
+ }
921
+
922
+
923
+ ## Contexto Externo
924
+
925
+ def build_external_context(artifacts, steps):
926
+
927
+ last_date = artifacts.df_raw["Date"].max()
928
+
929
+ news = get_colombia_news(last_date)
930
+ macro = get_colombia_macro()
931
+
932
+ return {
933
+ "fecha_corte": str(last_date),
934
+ "horizonte_dias": steps,
935
+ "noticias_colombia": news,
936
+ "macro_colombia": macro
937
+ }
938
+ # ============================================================
939
+ # AGENT RESPONSE
940
+ # ============================================================
941
+ def agent_answer(question: str, artifacts: TrainingArtifacts) -> Tuple[str, Optional[pd.DataFrame], Optional[plt.Figure], Optional[plt.Figure]]:
942
+ steps = extract_horizon(question)
943
+ catalog = build_answer_catalog(artifacts, steps=steps)
944
+ key = route_question(question)
945
+ base_answer = catalog[key]
946
+ analysis_text = ""
947
+
948
+ table = None
949
+ fig1 = None
950
+ fig2 = None
951
+
952
+ if key == "forecast":
953
+ outputs = forecast_targets(artifacts, steps=steps)
954
+ table = outputs["targets_forecast"][
955
+ ["Date", "Price_Equipo1_pred", "Equipo1_lower", "Equipo1_upper",
956
+ "Price_Equipo2_pred", "Equipo2_lower", "Equipo2_upper"]
957
+ ].copy()
958
+
959
+ fig1 = plot_target_forecast(artifacts, steps=steps, target="Equipo1")
960
+ fig2 = plot_target_forecast(artifacts, steps=steps, target="Equipo2")
961
+
962
+ analysis_text = interpret_results(question, artifacts, outputs)
963
+
964
+ base_answer = (
965
+ f"{base_answer}\n\n"
966
+ f"{analysis_text}\n\n"
967
+ "Se gener贸 el forecast de ambos equipos para el horizonte solicitado, incluyendo escenario central y bandas lower/upper."
968
+ )
969
+
970
+ elif key == "kpis_arima":
971
+ outputs = forecast_targets(artifacts, steps=steps)
972
+ table = outputs["x_z_forecast"][["Date", "Price_X", "X_lower", "X_upper", "Price_Z", "Z_lower", "Z_upper"]].copy()
973
+ fig1 = plot_xz_forecast(artifacts, steps=steps, commodity="X")
974
+ fig2 = plot_xz_forecast(artifacts, steps=steps, commodity="Z")
975
+
976
+ analysis_text = interpret_results(question, artifacts, outputs)
977
+
978
+ base_answer = (
979
+ f"{base_answer}\n\n"
980
+ f"{analysis_text}\n\n"
981
+ "Se incluyen las trayectorias proyectadas de X y Z con sus intervalos de confianza."
982
+ )
983
+
984
+ elif key == "shap":
985
+ table = pd.concat(
986
+ [
987
+ artifacts.shap_model1.head(10).assign(Modelo="Equipo 1"),
988
+ artifacts.shap_model2.head(10).assign(Modelo="Equipo 2"),
989
+ ],
990
+ ignore_index=True,
991
+ )
992
+
993
+ fig1 = plot_shap_bar(artifacts.shap_model1, "Top SHAP - Equipo 1")
994
+ fig2 = plot_shap_bar(artifacts.shap_model2, "Top SHAP - Equipo 2")
995
+
996
+ analysis_text = interpret_results(question, artifacts)
997
+
998
+ base_answer = (
999
+ f"{base_answer}\n\n"
1000
+ f"{analysis_text}\n\n"
1001
+ "Adem谩s de la tabla, se muestran los gr谩ficos de importancia SHAP para ambos modelos."
1002
+ )
1003
+
1004
+ elif key == "xgb":
1005
+ table = pd.concat(
1006
+ [
1007
+ artifacts.xgb_importance_model1.head(10).assign(Modelo="Equipo 1"),
1008
+ artifacts.xgb_importance_model2.head(10).assign(Modelo="Equipo 2"),
1009
+ ],
1010
+ ignore_index=True,
1011
+ )
1012
+
1013
+ analysis_text = interpret_results(question, artifacts)
1014
+
1015
+ base_answer = (
1016
+ f"{base_answer}\n\n"
1017
+ f"{analysis_text}\n\n"
1018
+ "La tabla resume las importancias internas de XGBoost para ambos modelos."
1019
+ )
1020
+
1021
+ elif key == "materias_primas":
1022
+ outputs = forecast_targets(artifacts, steps=steps)
1023
+ table = outputs["x_z_forecast"][["Date", "Price_X", "X_lower", "X_upper", "Price_Z", "Z_lower", "Z_upper"]].copy()
1024
+ fig1 = plot_commodities_forecast(artifacts, steps=steps)
1025
+ fig2 = None
1026
+
1027
+ analysis_text = interpret_results(question, artifacts, outputs)
1028
+
1029
+ base_answer = (
1030
+ f"{base_answer}\n\n"
1031
+ f"{analysis_text}\n\n"
1032
+ f"Se us贸 SARIMAX con order={SARIMA_ORDER} y seasonal_order={SARIMA_SEASONAL_ORDER}. "
1033
+ "El componente (1,1,1) captura autoregresi贸n, diferenciaci贸n y media m贸vil; el t茅rmino estacional (1,1,1,12) permite capturar un patr贸n repetitivo de periodicidad 12. "
1034
+ "Abajo se muestran las trayectorias proyectadas de X y Z con sus escenarios upper y lower."
1035
+ f"\n\nResumen SARIMAX X:\n{artifacts.sarimax_x_summary[:2000]}\n\nResumen SARIMAX Z:\n{artifacts.sarimax_z_summary[:2000]}"
1036
+ )
1037
+
1038
+ elif key == "correlacion":
1039
+ table = artifacts.top_corr.copy()
1040
+ fig1 = plot_correlation_heatmap(artifacts.top_corr)
1041
+ fig2 = None
1042
+
1043
+ base_answer = (
1044
+ f"{base_answer}\n\n"
1045
+ "Se muestra la matriz de correlaci贸n en tabla y heatmap para facilitar la interpretaci贸n visual de las relaciones entre X, Y, Z y los equipos."
1046
+ )
1047
+
1048
+ elif key == "eda":
1049
+ table = artifacts.top_corr.copy()
1050
+ base_answer += "\n\nLa matriz de correlaci贸n resumida se muestra en la tabla."
1051
+
1052
+ answer = llm_analyze(
1053
+ question=question,
1054
+ artifacts=artifacts,
1055
+ key=key,
1056
+ base_answer=base_answer,
1057
+ table=table,
1058
+ )
1059
+ return answer, table, fig1, fig2
1060
+
1061
+
1062
+ # =========================
1063
+ # GRADIO UI
1064
+ # =========================
1065
+ ARTIFACTS: Optional[TrainingArtifacts] = None
1066
+
1067
+ def responder(pregunta, history):
1068
+ global ART
1069
+
1070
+ try:
1071
+ if ART is None:
1072
+ ART = build_training_artifacts()
1073
+
1074
+ answer, table, fig1, fig2 = agent_answer(pregunta, ART)
1075
+
1076
+ if history is None:
1077
+ history = []
1078
+
1079
+ history = history + [
1080
+ {"role": "user", "content": pregunta},
1081
+ {"role": "assistant", "content": answer}
1082
+ ]
1083
+
1084
+ if table is None:
1085
+ table = pd.DataFrame()
1086
+
1087
+ return history, table, fig1, fig2, ""
1088
+
1089
+ except Exception as e:
1090
+ if history is None:
1091
+ history = []
1092
+
1093
+ history = history + [
1094
+ {"role": "user", "content": pregunta},
1095
+ {"role": "assistant", "content": f"Error: {str(e)}"}
1096
+ ]
1097
+
1098
+ return history, pd.DataFrame(), None, None, ""
1099
+
1100
+
1101
+ with gr.Blocks() as demo:
1102
+
1103
+ gr.Markdown("# 馃 Agente de Pron贸stico")
1104
+
1105
+ chatbot = gr.Chatbot()
1106
+ txt = gr.Textbox(label="Pregunta")
1107
+
1108
+ table = gr.Dataframe()
1109
+ fig1 = gr.Plot()
1110
+ fig2 = gr.Plot()
1111
+
1112
+ txt.submit(responder, [txt, chatbot], [chatbot, table, fig1, fig2, txt])
1113
+
1114
+ demo.launch()
historico_equipos.csv ADDED
The diff for this file is too large to render. See raw diff
 
requirements.txt ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ pandas
2
+ numpy
3
+ matplotlib
4
+ seaborn
5
+ statsmodels
6
+ scikit-learn
7
+ xgboost
8
+ shap
9
+ gradio
10
+ openai
11
+ requests