File size: 176,576 Bytes
60acce9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17f1192a-1d26-46c4-9712-e95080a5bb43",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 2] Failed to open local file 'bedmap_train.parquet'. Detail: [Windows error 2] The system cannot find the file specified.\r\n",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mrandom\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;66;03m#Only loading 1% of data\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;66;03m# Open the Parquet file\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m parquet_file = \u001b[43mpq\u001b[49m\u001b[43m.\u001b[49m\u001b[43mParquetFile\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mbedmap_train.parquet\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m     10\u001b[39m \u001b[38;5;66;03m# Get number of row groups\u001b[39;00m\n\u001b[32m     11\u001b[39m num_row_groups = parquet_file.num_row_groups\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\users\\adian\\applied machine learning course\\appmlenv\\Lib\\site-packages\\pyarrow\\parquet\\core.py:312\u001b[39m, in \u001b[36mParquetFile.__init__\u001b[39m\u001b[34m(self, source, metadata, common_metadata, read_dictionary, memory_map, buffer_size, pre_buffer, coerce_int96_timestamp_unit, decryption_properties, thrift_string_size_limit, thrift_container_size_limit, filesystem, page_checksum_verification)\u001b[39m\n\u001b[32m    309\u001b[39m filesystem, source = _resolve_filesystem_and_path(\n\u001b[32m    310\u001b[39m     source, filesystem, memory_map=memory_map)\n\u001b[32m    311\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m filesystem \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m312\u001b[39m     source = \u001b[43mfilesystem\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen_input_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    313\u001b[39m     \u001b[38;5;28mself\u001b[39m._close_source = \u001b[38;5;28;01mTrue\u001b[39;00m  \u001b[38;5;66;03m# We opened it here, ensure we close it.\u001b[39;00m\n\u001b[32m    315\u001b[39m \u001b[38;5;28mself\u001b[39m.reader = ParquetReader()\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\users\\adian\\applied machine learning course\\appmlenv\\Lib\\site-packages\\pyarrow\\_fs.pyx:787\u001b[39m, in \u001b[36mpyarrow._fs.FileSystem.open_input_file\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\users\\adian\\applied machine learning course\\appmlenv\\Lib\\site-packages\\pyarrow\\error.pxi:155\u001b[39m, in \u001b[36mpyarrow.lib.pyarrow_internal_check_status\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\users\\adian\\applied machine learning course\\appmlenv\\Lib\\site-packages\\pyarrow\\error.pxi:92\u001b[39m, in \u001b[36mpyarrow.lib.check_status\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[31mFileNotFoundError\u001b[39m: [WinError 2] Failed to open local file 'bedmap_train.parquet'. Detail: [Windows error 2] The system cannot find the file specified.\r\n"
     ]
    }
   ],
   "source": [
    "#data = pd.read_parquet('bedmap_train.parquet', engine='fastparquet') #Full data read.\n",
    "import pyarrow.parquet as pq\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "#Only loading 1% of data\n",
    "# Open the Parquet file\n",
    "parquet_file = pq.ParquetFile('bedmap_train.parquet')\n",
    "\n",
    "# Get number of row groups\n",
    "num_row_groups = parquet_file.num_row_groups\n",
    "\n",
    "# Select 1% of row groups randomly\n",
    "sample_size = max(1, int(num_row_groups * 0.01))\n",
    "selected_groups = random.sample(range(num_row_groups), sample_size)\n",
    "\n",
    "# Read only the selected row groups\n",
    "dfs = []\n",
    "for i in selected_groups:\n",
    "    table = parquet_file.read_row_group(i)\n",
    "    df = table.to_pandas()\n",
    "    dfs.append(df)\n",
    "\n",
    "# Combine into one DataFrame\n",
    "data = pd.concat(dfs, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e744f9c-22d4-440f-877b-23ad03b80291",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "536bfcdd-5f62-4f1b-a9ff-d1230dff608f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80cd9b6d-ef6b-43b9-bc57-fdb60c90312f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the figure size\n",
    "data.hist(bins=30, figsize=(15, 10), edgecolor='black')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "539929a7-8d38-4b32-a769-198bc66bd83a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Read the data and print the variables:\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m variables = \u001b[43mdata\u001b[49m.columns\n\u001b[32m      3\u001b[39m \u001b[38;5;28mprint\u001b[39m(variables.values)\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# Decide on which variables to use for input (X) and what defines the label (Y):\u001b[39;00m\n",
      "\u001b[31mNameError\u001b[39m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "# Read the data and print the variables:\n",
    "variables = data.columns\n",
    "print(variables.values)\n",
    "\n",
    "# Decide on which variables to use for input (X) and what defines the label (Y):\n",
    "input_variables = variables[(variables != 'ith_bm') & (variables != 'LON') & (variables != 'LAT') & (variables != 'geometry') & (variables != 'THICK')]\n",
    "input_data      = data[input_variables]\n",
    "truth_data      = data['THICK']\n",
    "benchmark_data  = data['ith_bm']\n",
    "print(\"  Variables used for training: \", input_variables.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6b31cdaf-1d00-4084-b3ea-be51688cced3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We start by wanting to use a Boosted Descision Tree:\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import lightgbm as lgb\n",
    "from lightgbm import early_stopping\n",
    "import time\n",
    "input_train, input_test, truth_train, truth_test, benchmark_train, benchmark_test = \\\n",
    "    train_test_split(input_data, truth_data, benchmark_data, test_size=0.25, random_state=42)\n",
    "\n",
    "# Feed the datasets to LightGBM:\n",
    "lgb_train = lgb.Dataset(input_train, truth_train)\n",
    "lgb_eval  = lgb.Dataset(input_test,  truth_test, reference=lgb_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "317f05e7-8c67-4de5-997f-a0c884d5c87f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.046077 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1530\n",
      "[LightGBM] [Info] Number of data points in the train set: 786432, number of used features: 6\n",
      "[LightGBM] [Info] Start training from score 1118.540039\n",
      "Training until validation scores don't improve for 20 rounds\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[100]\tvalid_0's l1: 159.879\n",
      "\n",
      "Time used by LightGBM: 7.4 s\n"
     ]
    }
   ],
   "source": [
    "# Set parameters for LightGBM (known more generally as \"hyper parameters\"):\n",
    "params = {\n",
    "    'boosting_type': 'gbdt', \n",
    "    'learning_rate': 0.1,     \n",
    "    'num_leaves': 6,         \n",
    "    'num_trees': 100,\n",
    "    'min_data_in_leaf': 20,  \n",
    "    'objective': 'mean_absolute_error',   # The outcome is binary, b-quark or not\n",
    "    'verbose': 1,            # Level of output. Can be set to -1 to suppress the output\n",
    "}\n",
    "\n",
    "# We track the time it takes to train the model:\n",
    "start=time.time()\n",
    "\n",
    "# Train the model:\n",
    "gbm = lgb.train(params,                             # General settings (defined above)\n",
    "                lgb_train,                          # Data to use for training\n",
    "                num_boost_round=1000,               # How many rounds for training\n",
    "                valid_sets=lgb_eval,                # Data to use for validation\n",
    "                callbacks=[early_stopping(20)])     # Stops if no improvement is seen in N=20 rounds.\n",
    " \n",
    "# Print the time usage:\n",
    "end = time.time()\n",
    "print(f\"\\nTime used by LightGBM: {(end-start):.1f} s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b88011eb-aca5-4846-b1bb-851ce6d8c3d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\adian\\applied machine learning course\\appmlenv\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE) LIGHTGBM: 159.8786\n",
      "Mean Absolute Error (MAE) BedMachine: 81.2041\n"
     ]
    }
   ],
   "source": [
    "# Scatter plot: predicted vs true\n",
    "truth_score = gbm.predict(input_test, num_iteration=gbm.best_iteration)  # Scores\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(truth_test, truth_score, alpha=0.2, label='LightGBM')\n",
    "plt.scatter(truth_test, benchmark_test, alpha=0.2, label='BedMachine')\n",
    "plt.plot([truth_test.min(), truth_test.max()], [truth_test.min(), truth_test.max()], 'r--')  # Ideal line\n",
    "plt.xlabel('True values')\n",
    "plt.ylabel('Predicted values')\n",
    "plt.title('Predicted vs. True')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Residual plot: true - predicted\n",
    "residuals = truth_test - truth_score\n",
    "residualsbenchmark = truth_test - benchmark_test\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.hist(residuals, bins=50, alpha=0.5, label='lightGBM')\n",
    "plt.hist(residualsbenchmark, bins=50, alpha=0.5, label='BedMachine')\n",
    "plt.xlabel(\"Residual (True - Predicted)\")\n",
    "plt.title(\"Residual Distribution\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mae = mean_absolute_error(truth_test, truth_score)\n",
    "maebenchmark = mean_absolute_error(truth_test, benchmark_test)\n",
    "print(f\"Mean Absolute Error (MAE) LIGHTGBM: {mae:.4f}\")\n",
    "print(f\"Mean Absolute Error (MAE) BedMachine: {maebenchmark:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "037955d8-90e9-4643-9c29-af6a964b5d98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87fc4d00-de7a-4dc9-acf3-f354c00b32ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b1461f58-6e37-4302-a288-da8c496215d3",
   "metadata": {},
   "source": [
    "Correlated data:\n",
    "\n",
    "Normalize data to avoid correlation.\n",
    "How do we figure out if points are correlated or not.\n",
    "\n",
    "BDT gives error around 83 meters \n",
    "\n",
    "Shouldconsider Bedmapmachine as a compeditor. You can also use it as an additional input if you want (though redifine the target as the depth divided by the Beddepth. The ratio will be around 1 which is a normalization for the neural network. Con: You become interdependent of updates of the Bedmachine model and you may inherit the errors of the bedmachine model. \n",
    "\n",
    "The points chosen must cover antartica (representative), maybe reweighting data thicknesses that are less occuring (this is likely not a problem though) and get uncorrelated points.\n",
    "\n",
    "Max thickness is 4500 meters.\n",
    "\n",
    "Another idea is pixelating (average in grid and so we can get a complete covering).\n",
    "\n",
    "\n",
    "Making test dataset of uncorrellated: Take a chunk out of map, train on everything else and test on chunck. Most of the chunck will be uncorrelated to the rest.\n",
    "\n",
    "11x11 center point is where thickness was measured.\n",
    "\n",
    "Try to introduce new datasets like temperature.\n",
    "\n",
    "GNN: Connect datapoints through flow of ice.\n",
    "\n",
    "Flow makes sense to make big images in CNN since information far away is relevant.\n",
    "\n",
    "For Nans in map put 0. \n",
    "\n",
    "Start with one map, and add more later if you can get some \n",
    "\n",
    "That model can also be used to filter for bad measurements out.\n",
    "\n",
    "\n",
    "\n",
    "Questions to Niccolo:\n",
    "CNN"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "appmlenv",
   "language": "python",
   "name": "appmlenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}