Upload app.py

app.py

@@ -225,7 +225,7 @@ def ode1(A0, B0, C0, temp, Ea, A_factor):
 results = []
 
 counter = 0
-while counter < 15000:
+while counter < 100000:
     counter += 1
 
     A0 = round(random.uniform(1.0, 10.0), 2)
@@ -272,7 +272,7 @@ df_train
 results = []
 
 counter = 0
-while counter < 5000:
+while counter < 20000:
     counter += 1
 
     A0 = round(random.uniform(1.0, 10.0), 2)
@@ -358,131 +358,131 @@ X_test_scaled = scaler.transform(X_test)
 
 """## Models"""
 
-from sklearn.metrics import accuracy_score
+# from sklearn.metrics import accuracy_score
 
 """### Logistic Regression"""
 
-from sklearn.linear_model import LogisticRegression
+# from sklearn.linear_model import LogisticRegression
 
-lr = LogisticRegression(max_iter=1000, C=10, penalty='l2')
-lr.fit(X_train_scaled, y_train)
-lr_pred = lr.predict(X_test_scaled)
+# lr = LogisticRegression(max_iter=1000, C=10, penalty='l2')
+# lr.fit(X_train_scaled, y_train)
+# lr_pred = lr.predict(X_test_scaled)
 
-print("Logistic Regression Accuracy:", accuracy_score(y_test, lr_pred))
+# print("Logistic Regression Accuracy:", accuracy_score(y_test, lr_pred))
 
 """### RandomForestClassifier"""
 
-from sklearn.ensemble import RandomForestClassifier
+# from sklearn.ensemble import RandomForestClassifier
 
-rf = RandomForestClassifier(class_weight='balanced', random_state=42, n_estimators=200, max_depth=None)
-rf.fit(X_train, y_train)
-rf_pred = rf.predict(X_test)
+# rf = RandomForestClassifier(class_weight='balanced', random_state=42, n_estimators=200, max_depth=None)
+# rf.fit(X_train, y_train)
+# rf_pred = rf.predict(X_test)
 
-print("RandomForestClassifier Accuracy:", accuracy_score(y_test, rf_pred))
+# print("RandomForestClassifier Accuracy:", accuracy_score(y_test, rf_pred))
 
 """### Gradient Boosting Classifier"""
 
-from sklearn.ensemble import GradientBoostingClassifier
+# from sklearn.ensemble import GradientBoostingClassifier
 
-gb = GradientBoostingClassifier(n_estimators=200, max_depth=5, random_state=42)
-gb.fit(X_train, y_train)
-gb_pred = gb.predict(X_test)
+# gb = GradientBoostingClassifier(n_estimators=200, max_depth=5, random_state=42)
+# gb.fit(X_train, y_train)
+# gb_pred = gb.predict(X_test)
 
-print("Gradient Boosting Accuracy:", accuracy_score(y_test, gb_pred))
+# print("Gradient Boosting Accuracy:", accuracy_score(y_test, gb_pred))
 
 """### Support Vector Classifier"""
 
-from sklearn.svm import SVC
+# from sklearn.svm import SVC
 
-svc = SVC(C=10, kernel='rbf', class_weight='balanced')
-svc.fit(X_train_scaled, y_train)
-svc_pred = svc.predict(X_test_scaled)
+# svc = SVC(C=10, kernel='rbf', class_weight='balanced')
+# svc.fit(X_train_scaled, y_train)
+# svc_pred = svc.predict(X_test_scaled)
 
-print("SVC Accuracy:", accuracy_score(y_test, svc_pred))
+# print("SVC Accuracy:", accuracy_score(y_test, svc_pred))
 
 """### K-Nearest Neighbors"""
 
-from sklearn.neighbors import KNeighborsClassifier
+# from sklearn.neighbors import KNeighborsClassifier
 
-knn = KNeighborsClassifier(n_neighbors=7, weights='uniform')
-knn.fit(X_train_scaled, y_train)
-knn_pred = knn.predict(X_test_scaled)
+# knn = KNeighborsClassifier(n_neighbors=7, weights='uniform')
+# knn.fit(X_train_scaled, y_train)
+# knn_pred = knn.predict(X_test_scaled)
 
-print("KNN Accuracy:", accuracy_score(y_test, knn_pred))
+# print("KNN Accuracy:", accuracy_score(y_test, knn_pred))
 
 """### XG Boost"""
 
-from xgboost import XGBClassifier
+# from xgboost import XGBClassifier
 
-xgb_model = XGBClassifier(learning_rate=0.1, max_depth=7, n_estimators=200, eval_metric='mlogloss', random_state=42)
-xgb_model.fit(X_train, y_train)
-xgb_pred = xgb_model.predict(X_test)
+# xgb_model = XGBClassifier(learning_rate=0.1, max_depth=7, n_estimators=200, eval_metric='mlogloss', random_state=42)
+# xgb_model.fit(X_train, y_train)
+# xgb_pred = xgb_model.predict(X_test)
 
-print("XGBoost Accuracy:", accuracy_score(y_test, xgb_pred))
+# print("XGBoost Accuracy:", accuracy_score(y_test, xgb_pred))
 
 """### Hyperparameter tuning"""
 
-from sklearn.linear_model import LogisticRegression
-from sklearn.svm import SVC
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
-import xgboost as xgb
-
-models = {
-    'LogisticRegression': LogisticRegression(class_weight='balanced', max_iter=1000),
-    'SVC': SVC(class_weight='balanced'),
-    'KNN': KNeighborsClassifier(),
-    'RandomForest': RandomForestClassifier(class_weight='balanced', random_state=42),
-    'GradientBoosting': GradientBoostingClassifier(random_state=42),
-    'XGBoost': xgb.XGBClassifier(eval_metric='mlogloss', random_state=42)
-}
-
-
-param_grids = {
-    'LogisticRegression': {
-        'C': [0.1, 1, 10],
-        'penalty': ['l2']
-    },
-    'SVC': {
-        'C': [0.1, 1, 10],
-        'kernel': ['linear', 'rbf']
-    },
-    'KNN': {
-        'n_neighbors': [3, 5, 7],
-        'weights': ['uniform', 'distance']
-    },
-    'RandomForest': {
-        'n_estimators': [100, 200],
-        'max_depth': [5, 10, None]
-    },
-    'GradientBoosting': {
-        'n_estimators': [100, 200],
-        'max_depth': [3, 5, 7]
-    },
-    'XGBoost': {
-        'n_estimators': [100, 200],
-        'max_depth': [3, 5, 7],
-        'learning_rate': [0.05, 0.1]
-    }
-}
-
-from sklearn.model_selection import GridSearchCV
-
-best_models = {}
-
-for name, model in models.items():
-    print(f"Running GridSearch for {name}...")
-    grid = GridSearchCV(model, param_grids[name], cv=5, scoring='accuracy')
-
-    if name in ['LogisticRegression', 'SVC', 'KNN']:
-        grid.fit(X_train_scaled, y_train)
-    else:
-        grid.fit(X_train, y_train)
-
-    best_models[name] = grid.best_estimator_
-    print(f"Best params for {name}:", grid.best_params_)
-    print("Best CV Score:", grid.best_score_)
-    print("=====================================")
+# from sklearn.linear_model import LogisticRegression
+# from sklearn.svm import SVC
+# from sklearn.neighbors import KNeighborsClassifier
+# from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+# import xgboost as xgb
+
+# models = {
+#     'LogisticRegression': LogisticRegression(class_weight='balanced', max_iter=1000),
+#     'SVC': SVC(class_weight='balanced'),
+#     'KNN': KNeighborsClassifier(),
+#     'RandomForest': RandomForestClassifier(class_weight='balanced', random_state=42),
+#     'GradientBoosting': GradientBoostingClassifier(random_state=42),
+#     'XGBoost': xgb.XGBClassifier(eval_metric='mlogloss', random_state=42)
+# }
+
+
+# param_grids = {
+#     'LogisticRegression': {
+#         'C': [0.1, 1, 10],
+#         'penalty': ['l2']
+#     },
+#     'SVC': {
+#         'C': [0.1, 1, 10],
+#         'kernel': ['linear', 'rbf']
+#     },
+#     'KNN': {
+#         'n_neighbors': [3, 5, 7],
+#         'weights': ['uniform', 'distance']
+#     },
+#     'RandomForest': {
+#         'n_estimators': [100, 200],
+#         'max_depth': [5, 10, None]
+#     },
+#     'GradientBoosting': {
+#         'n_estimators': [100, 200],
+#         'max_depth': [3, 5, 7]
+#     },
+#     'XGBoost': {
+#         'n_estimators': [100, 200],
+#         'max_depth': [3, 5, 7],
+#         'learning_rate': [0.05, 0.1]
+#     }
+# }
+
+# from sklearn.model_selection import GridSearchCV
+
+# best_models = {}
+
+# for name, model in models.items():
+#     print(f"Running GridSearch for {name}...")
+#     grid = GridSearchCV(model, param_grids[name], cv=5, scoring='accuracy')
+
+#     if name in ['LogisticRegression', 'SVC', 'KNN']:
+#         grid.fit(X_train_scaled, y_train)
+#     else:
+#         grid.fit(X_train, y_train)
+
+#     best_models[name] = grid.best_estimator_
+#     print(f"Best params for {name}:", grid.best_params_)
+#     print("Best CV Score:", grid.best_score_)
+#     print("=====================================")
 
 """### BEST PARAMS
 ==========================================================================
@@ -611,7 +611,7 @@ classifier = tf.estimator.DNNClassifier(
 
 classifier.train(
     input_fn=lambda: input_fn(train_normalized, train_y_encoded, training=True),
-    steps=1000
+    steps=3000
 )
 
 test_y_encoded = le.fit_transform(test_y) #we used sckit label encoder to encode the values better than 1 2 3 4 5
@@ -890,7 +890,4 @@ iface.launch(debug=True)
 
 # get_ipython().run_line_magic('shell', 'curl https://loca.lt/mytunnelpassword') #getting ur home pass 🥶
 
-# !npx localtunnel --port 8501 #the tunnel
-
-
-
+# !npx localtunnel --port 8501 #the tunnel