File size: 19,006 Bytes
ba67510 f736b41 ba67510 f736b41 ba67510 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 |
import pandas as pd
import numpy as np
import streamlit as st
from sklearn.impute import KNNImputer,SimpleImputer,IterativeImputer
import best_tts, evaluationer,models
from sklearn.experimental import enable_iterative_imputer
from sklearn.model_selection import train_test_split as tts
from collections import Counter
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import root_mean_squared_error
import seaborn as sns
from sklearn.decomposition import PCA
import grid_search_cv
import matplotlib.pyplot as plt
import outliers,best_tts
import feature_selections
def Auto_optimizer(X,y,eva,model,model_name,test= None):
if st.button("Train Regression Model"):
num_cols = X.select_dtypes(exclude = "O").columns
cat_cols = X.select_dtypes(include = "O").columns
st.write("Num_cols",tuple(num_cols))
st.write("cat_cols",tuple(cat_cols))
# check for Duplicate and drop duplicated in X
if len(X.isnull().sum()[(X.isnull().sum()/len(X)*100) >40]) >0:
X = X.drop(columns = X.isnull().sum()[(X.isnull().sum()/len(X)*100) >40].index)
st.write("Columns with more than 40% null values removed")
# st.write("csx",X)
len_null = X.isnull().sum().sum()
st.write(f"There are {len_null} null values in Train")
knn_imputed_num_X = X.copy()
si_mean_imputed_num_X = X.copy()
# st.write("sf",si_mean_imputed_num_X)
si_median_imputed_num_X = X.copy()
si_most_frequent_imputed_num_X = X.copy()
iter_imputed_num_X = X.copy()
knn_imputed_X_cat_dropped = knn_imputed_num_X.copy()
si_mean_imputed_X_cat_dropped = si_mean_imputed_num_X.copy()
si_median_imputed_X_cat_dropped = si_median_imputed_num_X.copy()
si_most_frequent_imputed_X_cat_dropped = si_most_frequent_imputed_num_X.copy()
iter_imputed_X_cat_dropped = iter_imputed_num_X.copy()
if len_null >0:
if X[num_cols].isnull().sum().sum() >0:
knn_imputer = KNNImputer(n_neighbors = 5)
knn_imputed_num_X[num_cols] = knn_imputer.fit_transform(knn_imputed_num_X[num_cols])
si_imputer = SimpleImputer(strategy = "mean")
si_mean_imputed_num_X[num_cols] = si_imputer.fit_transform(si_mean_imputed_num_X[num_cols])
si_imputer = SimpleImputer(strategy = "median")
si_median_imputed_num_X[num_cols] = si_imputer.fit_transform(si_median_imputed_num_X[num_cols])
si_imputer = SimpleImputer(strategy = "most_frequent")
si_most_frequent_imputed_num_X[num_cols] = si_imputer.fit_transform(si_most_frequent_imputed_num_X[num_cols])
iter_imputer = IterativeImputer(max_iter = 200,random_state= 42)
iter_imputed_num_X[num_cols] = iter_imputer.fit_transform(iter_imputed_num_X[num_cols])
knn_imputed_X_cat_dropped = knn_imputed_num_X.copy()
si_mean_imputed_X_cat_dropped = si_mean_imputed_num_X.copy()
si_median_imputed_X_cat_dropped = si_median_imputed_num_X.copy()
si_most_frequent_imputed_X_cat_dropped = si_most_frequent_imputed_num_X.copy()
iter_imputed_X_cat_dropped = iter_imputed_num_X.copy()
if X[cat_cols].isnull().sum().sum() >0:
# treating missing values in categorical columns
# st.write("si_mean_imputed_num_X",si_mean_imputed_num_X)
si_imputer = SimpleImputer(strategy = "most_frequent")
knn_imputed_num_X[cat_cols] = si_imputer.fit_transform(knn_imputed_num_X[cat_cols])
si_imputer = SimpleImputer(strategy = "most_frequent")
si_mean_imputed_num_X.loc[:,cat_cols] = si_imputer.fit_transform(si_mean_imputed_num_X.loc[:,cat_cols])
# st.write("si_mean_imputed_num_X",si_mean_imputed_num_X)
si_median_imputed_num_X[cat_cols] = si_imputer.fit_transform(si_median_imputed_num_X[cat_cols])
si_most_frequent_imputed_num_X[cat_cols] = si_imputer.fit_transform(si_most_frequent_imputed_num_X[cat_cols])
iter_imputed_num_X[cat_cols] = si_imputer.fit_transform(iter_imputed_num_X[cat_cols])
knn_imputed_X_cat_dropped = knn_imputed_X_cat_dropped.dropna()
si_mean_imputed_X_cat_dropped =si_mean_imputed_X_cat_dropped.dropna()
si_median_imputed_X_cat_dropped =si_median_imputed_X_cat_dropped.dropna()
si_most_frequent_imputed_X_cat_dropped =si_most_frequent_imputed_X_cat_dropped.dropna()
iter_imputed_X_cat_dropped =iter_imputed_X_cat_dropped.dropna()
miss_val_dropped_X = X.dropna()
# list of dataframes
list_X_after_missing_values= [knn_imputed_num_X,
si_mean_imputed_num_X,
si_median_imputed_num_X,
si_most_frequent_imputed_num_X,
iter_imputed_num_X,
knn_imputed_X_cat_dropped,
si_mean_imputed_X_cat_dropped,
si_median_imputed_X_cat_dropped,
si_most_frequent_imputed_X_cat_dropped,
iter_imputed_X_cat_dropped,
miss_val_dropped_X]
list_X_after_missing_values_names= ["knn_imputed_num_X",
"si_mean_imputed_num_X",
"si_median_imputed_num_X",
"si_most_frequent_imputed_num_X",
"iter_imputed_num_X",
"knn_imputed_X_cat_dropped",
"si_mean_imputed_X_cat_dropped",
"si_median_imputed_X_cat_dropped",
"si_most_frequent_imputed_X_cat_dropped",
"iter_imputed_X_cat_dropped",
"miss_val_dropped_X"]
# st.write("si_most_frequent_imputed_num_X",si_most_frequent_imputed_num_X,)
ord_enc_cols = []
ohe_enc_cols = []
if len(cat_cols) == 0:
st.write("No Categorical Columns in Train")
else:
st.write("Select Columns for Ordinal Encoding")
for column in cat_cols:
selected = st.checkbox(column)
if selected:
st.write(f"No. of Unique value in {column} column are", X[column].nunique())
ord_enc_cols.append(column)
ohe_enc_cols = set(cat_cols) -set(ord_enc_cols)
ohe_enc_cols = list(ohe_enc_cols)
if len(ord_enc_cols)>0:
st.write("ordinal encoded columns" ,tuple(ord_enc_cols))
if len(ohe_enc_cols)>0:
st.write("one hot encoded columns" ,tuple(ohe_enc_cols))
if len(ord_enc_cols)>0:
ordinal_order_vals = []
for column in ord_enc_cols:
unique_vals = X.dropna()[column].unique()
# st.write(f"No. of Unique value in {column} column are", len(unique_vals))
ordered_unique_vals = st.multiselect("Select values in order for Ordinal Encoding",unique_vals,unique_vals)
ordinal_order_vals.append(ordered_unique_vals)
st.write("order of values for Ordinal Encoding",tuple(ordinal_order_vals))
if len_null > 0:
for df_name, df in enumerate(list_X_after_missing_values):
# st.write(f"{list_X_after_missing_values_names[df_name]}",df)
from sklearn.preprocessing import OrdinalEncoder
ord = OrdinalEncoder(categories=ordinal_order_vals,handle_unknown= "use_encoded_value",unknown_value = -1 )
df[ord_enc_cols] = ord.fit_transform(df[ord_enc_cols])
# st.write(f"{list_X_after_missing_values_names[df_name]}",df)
else :
from sklearn.preprocessing import OrdinalEncoder
ord = OrdinalEncoder(categories=ordinal_order_vals,handle_unknown= "use_encoded_value",unknown_value = -1 )
X[ord_enc_cols] = ord.fit_transform(X[ord_enc_cols])
st.write("Ordinal Encoding Completed ✅")
if len(ohe_enc_cols)>0:
if len_null > 0:
for df_name, df in enumerate(list_X_after_missing_values):
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(sparse_output = False,handle_unknown = "ignore")
pd.options.mode.chained_assignment = None
df.loc[:, ohe.get_feature_names_out()] = ohe.fit_transform(df[ohe_enc_cols])
df.drop(columns = ohe_enc_cols,inplace = True)
pd.options.mode.chained_assignment = 'warn'
else:
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(sparse_output = False,handle_unknown = "ignore")
pd.options.mode.chained_assignment = None
X.loc[:, ohe.get_feature_names_out()] = ohe.fit_transform(X[ohe_enc_cols])
X.drop(columns = ohe_enc_cols,inplace = True)
pd.options.mode.chained_assignment = 'warn'
st.write("OneHot Encoding Completed ✅")
if len(ohe_enc_cols)>0:
if len_null > 0:
for name,df in enumerate(list_X_after_missing_values):
X_train,X_test,y_train,y_test = tts(df,y[df.index],test_size =.2 ,random_state = 42)
# best_tts.best_tts(df,y,model,eva)
evaluationer.evaluation(f"{list_X_after_missing_values_names[name]}",X_train,X_test,y_train,y_test,model,root_mean_squared_error,eva)
else:
X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size =.2 ,random_state = 42)
# best_tts.best_tts(X,y,model,eva)
evaluationer.evaluation(f"baseline_model",X_train,X_test,y_train,y_test,model,root_mean_squared_error,eva)
if len_null >0:
for name,df in enumerate(list_X_after_missing_values):
X_train,X_test,y_train,y_test = tts(df,y[df.index],test_size =.2 ,random_state = 42)
evaluationer.evaluation(f"{list_X_after_missing_values_names[name]}",X_train,X_test,y_train,y_test,model,root_mean_squared_error,eva)
if eva == "class":
counter = Counter(y)
total = sum(counter.values())
balance_ratio = {cls: count / total for cls, count in counter.items()}
num_classes = len(balance_ratio)
ideal_ratio = 1 / num_classes
a = all(abs(ratio - ideal_ratio) <= 0.1 * ideal_ratio for ratio in balance_ratio.values())
if a == True:
st.write("Balanced Dataset ✅")
st.write("Using accuracy for Evaluation")
value = "test_acc"
else:
st.write("Unbalanced Dataset ❌")
st.write("Using F1 score for Evaluation")
value = "test_f1"
evaluationer.classification_evaluation_df.sort_values(by = value,inplace= True)
name = str(evaluationer.classification_evaluation_df.iloc[-1,0])
st.write("df name",evaluationer.classification_evaluation_df.iloc[-1,0])
if len_null >0:
b = list_X_after_missing_values_names.index(name)
st.write("df",list_X_after_missing_values[b])
X = list_X_after_missing_values[b]
if eva == "reg":
st.write("Using R2 score for Evaluation",evaluationer.reg_evaluation_df)
value = "test_r2"
evaluationer.reg_evaluation_df.sort_values(by = value,inplace= True)
name = str(evaluationer.reg_evaluation_df.iloc[-1,0])
if len_null >0:
b = list_X_after_missing_values_names.index(name)
st.write("df",list_X_after_missing_values[b])
X = list_X_after_missing_values[b]
# Create a figure and axes
num_plots = len(num_cols)
cols = 2 # Number of columns in the subplot grid
rows = (num_plots + cols - 1) // cols # Calculate the number of rows needed
fig, axes = plt.subplots(rows, cols, figsize=(15, 5 * rows))
# Flatten the axes array for easy iteration, and remove any excess subplots
axes = axes.flatten()
for ax in axes[num_plots:]:
fig.delaxes(ax)
for i, col in enumerate(num_cols):
sns.histplot(X[col], ax=axes[i],kde = True,color=sns.color_palette('Oranges', as_cmap=True)(0.7))
axes[i].set_title(col)
# Adjust layout
plt.tight_layout()
# Show the plot in Streamlit
st.pyplot(fig)
# Create a figure and axes
num_plots = len(num_cols)
cols = 3 # Number of columns in the subplot grid
rows = (num_plots + cols - 1) // cols # Calculate the number of rows needed
fig, axes = plt.subplots(rows, cols, figsize=(15, 5 * rows))
# Flatten the axes array for easy iteration, and remove any excess subplots
axes = axes.flatten()
for ax in axes[num_plots:]:
fig.delaxes(ax)
for i, col in enumerate(num_cols):
sns.boxplot(y=X[col], ax=axes[i],palette="magma")
axes[i].set_title(col)
# Adjust layout
plt.tight_layout()
# Show the plot in Streamlit
st.pyplot(fig)
outlier_cols = st.multiselect("De-Select columns for Detecting Outliers", num_cols,default= list(num_cols))
st.write("Checking for Outliers")
outliers_df_X,outlier_indexes = outliers.detect_outliers(X,list(outlier_cols))
st.write("Outliers in Dataframe Summary",outliers_df_X)
st.write("Columns for Outliers handling",tuple(outliers_df_X["columns name"]))
select_outlier_cols = st.multiselect("Select columns for Outlier Handling",tuple(outliers_df_X["columns name"]),default =tuple(outliers_df_X["columns name"]))
resultant,outlier_handled_df,outlier_handled_df_name= outliers.outlier_handling(X,y,model,outlier_indexes = outlier_indexes,outlier_cols = select_outlier_cols ,method = root_mean_squared_error,test_size = 0.2, random_state = 42,eva = eva)
st.write("outlier handling with methods",resultant)
st.write("Best method with outlier handling",resultant.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0])
try :
st.write("Best X Data Index No.",outlier_handled_df_name.index(resultant.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0]))
st.write("Best X DataFrame after outlier handling ",outlier_handled_df[outlier_handled_df_name.index(resultant.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0])])
X = outlier_handled_df[outlier_handled_df_name.index(resultant.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0])]
except :
"evaluation of baseline model is better continuing with baseline model"
X_train,X_test,y_train,y_test = tts(X,y[X.index],random_state = 42,test_size = 0.2)
st.write("result_df",X)
if eva == "reg":
try:
result_df_1 , feature_col, feature_col_name = feature_selections.feature_selection(X_train,X_test,y_train,y_test,model,alpha = 0.05)
X = X.drop(columns = feature_col[feature_col_name.index(result_df_1.sort_values(by = "test_r2").tail(1).iloc[:,0].values[0])])
except:
"evaluation by feature selection is not better than previous"
try:
result,X_train_b,X_test_b,y_train_b,y_test_b = best_tts.best_tts(X,y,model,eva)
st.write("result_df",result)
except:
X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size =0.2,random_state = 42)
elif eva =="class":
try:
result_df_1 , feature_col, feature_col_name = feature_selections.clas_feature_selection(X_train,X_test,y_train,y_test,model)
X = X.drop(columns = feature_col[feature_col_name.index(result_df_1.sort_values(by = "test_acc").tail(1).iloc[:,0].values[0])])
except:
"evaluation by feature selection is not better than previous"
try:
result,X_train_b,X_test_b,y_train_b,y_test_b = best_tts.best_tts(X,y,model,eva)
st.write("result_df",result)
except:
X_train,X_test,y_train,y_test = tts(X,y[X.index],test_size =0.2,random_state = 42)
st.write("cheking with polynomial features")
poly = PolynomialFeatures(degree=(2))
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.transform(X_test)
result_df_2 = evaluationer.evaluation("polynomial features degree 2",X_train_poly,X_test_poly,y_train,y_test,model,root_mean_squared_error,eva)
st.write("after polynomial features degree 2",evaluationer.reg_evaluation_df)
poly1 = PolynomialFeatures(degree=(3))
X_train_poly1 = poly.fit_transform(X_train)
X_test_poly1 = poly.transform(X_test)
evaluationer.evaluation("polynomial features degree 3",X_train_poly1,X_test_poly1,y_train,y_test,model,root_mean_squared_error,eva)
st.write("after polynomial features degree 3",evaluationer.reg_evaluation_df)
pca = PCA(n_components=0.95)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
evaluationer.evaluation("PCA",X_train_pca,X_test_pca,y_train,y_test,model,root_mean_squared_error,eva)
st.write("After PCA",evaluationer.reg_evaluation_df)
grid_search_cv.perform_grid_search(model,model_name,X_train,X_test,y_train,y_test,eva)
st.write("best param",evaluationer.reg_evaluation_df)
st.sidebar.button("click to clear evaluation metrics",evaluationer.reg_evaluation_df.drop(index = evaluationer.reg_evaluation_df.index))
|