renamed hyperparams to gscv

earlier_versions/my_datap1.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 24 10:48:10 2022
+
+@author: tanu
+"""
+###############################################################################
+# questions:
+ # which data to use: merged_df3 or merged_df2
+ # which is the target? or_mychisq or drtype col
+ # scaling: can it be from -1 to 1?
+ 
+# strategy:
+    # available data = X_train
+    # available data but NAN = validation_test
+    # test data: mut generated not in mcsm
+
+###############################################################################
+import os, sys
+import re
+from sklearn.datasets import load_boston
+from sklearn import linear_model
+from sklearn import preprocessing
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+print(np.__version__)
+print(pd.__version__)
+#%% read data
+homedir = os.path.expanduser("~")
+os.chdir(homedir + "/git/ML_AI_training/test_data")
+
+# this needs to be merged_df2 or merged_df3?
+my_df = pd.read_csv("pnca_all_params.csv")
+
+my_df.dtypes
+my_df_cols = my_df.columns
+
+omit_cols1 = ['pdb_file'
+             , 'seq_offset4pdb'
+             , 'mut_3upper'
+             , 'wild_pos'
+             , 'wild_chain_pos'
+             , 'chain'
+             , 'wt_3upper'
+             , 'consurf_colour'
+             , 'consurf_colour_rev'
+             , 'consurf_msa_data'
+             , 'consurf_aa_variety'
+             , 'snap2_accuracy_pc'
+             , 'beta_logistic'
+             , 'se_logistic'
+             , 'zval_logisitc'
+             , 'pval_chisq'
+             , 'log10_or_mychisq'
+             , 'neglog_pval_fisher'
+             , 'wild_type'
+             , 'mutant_type'
+             , 'position'
+             , 'ligand_id'
+             , 'mutation'
+             , 'ss'
+             , 'ss_class' # include it later?
+             ]
+
+omit_cols2 = list(my_df.columns[my_df.columns.str.contains(".*ci_.*") | my_df.columns.str.contains(".*_scaled*") | my_df.columns.str.contains(".*_outcome*")])
+
+# [WATCH:] just to test since these have negative values!
+omit_cols3 = list(my_df.columns[my_df.columns.str.contains("electro_.*") | my_df.columns.str.contains("disulfide_.*") | my_df.columns.str.contains("hbonds_.*") | my_df.columns.str.contains("partcov_.*") | my_df.columns.str.contains("vdwclashes.*") | my_df.columns.str.contains("volumetric.*")])
+
+omit_cols = omit_cols1 + omit_cols2 + omit_cols3
+
+my_df_filt = my_df.loc[:, ~my_df.columns.isin(omit_cols)]
+my_df_filt_cols = my_df_filt.columns
+
+foo = my_df_filt['or_mychisq'].value_counts() 
+foo = foo.to_frame()
+########################
+# [WATCH]: Drop na
+my_df2 = my_df_filt.dropna()
+my_df2['resistance'] = my_df2['or_mychisq'].apply(lambda x: 0 if x <=1 else 1)
+my_df2['resistance'].value_counts()
+y = my_df2['resistance']
+
+#==============================================================================
+omit_cols_y = ['or_mychisq', 'resistance']
+my_df_ml = my_df2.loc[:, ~my_df2.columns.isin(omit_cols_y)]
+#%%############################################################################
+X_train = my_df_ml.set_index('mutationinformation')
+X_train = X_train.iloc[:,:4]
+y_train  = y
+#X_train = X_train.dropna()
+#y_train = y.dropna()
+
+# check dim
+X_train.shape
+y_train.shape
+
+#%%=====================================================
+grid = sns.PairGrid(data = pd.concat([X_train
+                                    , pd.Series(y_train , name = "resistance")]
+                                    , axis = 1))
+
+grid.map_offdiag(sns.scatterplot)
+grid.map_diag(sns.distplot)
+plt.show()
+
+model = linear_model.LinearRegression()
+model.fit(X_train, y_train)
+
+###################
+#  test set
+X_test = my_df[my_df['or_mychisq'].isnull()]
+#X_test =[ X_test.iloc[:,:4]]
+# HARD part?
+# what should be the test set?
+X_test = [23.9, 0.69, -0.16, 0.59
+    , 5, 0.5, 0.4, -1
+    , 0.1, 1, 1, 1] 
+X_test_re = np.array(X_test).reshape(3, -1)
+
+
+####################
+fitted = model.predict(X_train)
+model.coef_
+model.predict(X_test_re)
+resid = y_train - fitted
+resid
+
+#####################
+from sklearn import preprocessing
+scaler = preprocessing.MinMaxScaler()
+scaler.fit(X_train)
+#We can then create a scaled training set
+X_train_scaled = scaler.transform(X_train)
+new_scaled = scaler.transform(X_test_re)
+model.predict(new_scaled)
+#########
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LogisticRegression
+# model_pipe = Pipeline(steps = [('preprocess', preprocessing.MinMaxScaler())
+#                           ,('regression', linear_model.LinearRegression())
+#     ])
+model_pipe = Pipeline(steps = [('preprocess', preprocessing.MinMaxScaler())
+                              ,('logis', LogisticRegression(class_weight = 'balanced'))
+                          ])
+
+model_pipe.fit(X_train,y_train)
+fitted_vals = model_pipe.predict(X_train)
+# gives the array of predictions
+model_pipe.predict(X_test_re)
+
+# for Linear Reg only
+# resid = y_train - fitted_vals
+# resid  
+
+#=====
+# Logistic  1 test
+# FAILS since: the test set dim and input dim should be the same
+# i.e if you give the model 10 features to train on, you will need
+# 10 features to predict something?
+# THINK!!!!
+#=====
+mod_logis = linear_model.LogisticRegression(class_weight = 'balanced')
+mod_logis.fit(X_train,y_train)
+X_test = [23.9] 
+X_test_re = np.array(X_test).reshape(1, -1)
+mod_logis.predict(X_test_re)
+#################
+
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+y_pred = model_pipe.predict(X_train)
+accuracy_score(y_train,y_pred)
+precision_score(y_train,y_pred,pos_label=1)# tp/(tp + fp) 
+recall_score(y_train,y_pred,pos_label=1) # tp/(tp + fn)
+########
+
+# WORKS!
+from sklearn.model_selection import cross_validate
+from sklearn.metrics import make_scorer
+import pandas as pd
+acc = make_scorer(accuracy_score)
+def precision(y_true,y_pred):
+    return precision_score(y_true,y_pred,pos_label = 1) #0
+
+def recall(y_true,y_pred):
+    return recall_score(y_true, y_pred, pos_label = 1) #0
+
+prec = make_scorer(precision)
+rec = make_scorer(recall)
+output = cross_validate(model_pipe
+                        , X_train
+                        , y_train
+                        , scoring = {'acc' : acc
+                                     ,'prec' : prec
+                                     ,'rec' : rec}
+                        , cv = 10, return_train_score = False)
+
+pd.DataFrame(output).mean()
+# fit_time      0.005486
+# score_time    0.002673
+# test_acc      0.601799
+# test_prec     0.976936
+# test_rec      0.603226
+# dtype: float64
+
+# the three scores
+# 0.65527950310559
+# 0.9853658536585366
+# 0.6516129032258065