Update train.py and model.py

sstar/models.py

@@ -19,7 +19,7 @@
 import numpy as np
 
 
-def train_logistic_regression(df, model_file):
+def train_logistic_regression(train_df, model_file):
     """
     Description:
         Function for training of the statsmodels logistic classification.
@@ -29,13 +29,20 @@ def train_logistic_regression(df, model_file):
         save_filename str: filename for output model
     """
     sm_data_exog = train_df.copy()
-    sm_data_exog.drop(["label"], axis=1, inplace=True)
-    sm_data_exog.replace(np.nan, 0, inplace=True)
+    sm_data_exog.drop(['label'], axis=1, inplace=True)
     sm_data_exog = sm.add_constant(sm_data_exog, prepend=False)
 
-    sm_data_endog = train_df["label"]
+    sm_data_endog = train_df['label']
 
     glm_binom = sm.GLM(sm_data_endog.astype(int), sm_data_exog.astype(float),family=sm.families.Binomial())
     result = glm_binom.fit()
 
-    result.save(save_filename)
+    result.save(model_file)
+
+
+def train_sstar():
+    pass
+
+
+def train_extra_trees():
+    pass

sstar/train.py

@@ -19,6 +19,7 @@
 import pandas as pd
 from multiprocessing import Process, Queue
 from sstar.preprocess import process_data
+from sstar.models import train_logistic_regression
 
 
 def train(demo_model_file, nrep, nref, ntgt, ref_id, tgt_id, src_id, seq_len, mut_rate, rec_rate, thread, output_prefix, output_dir, algorithm=None, seed=None):
@@ -57,8 +58,12 @@ def _train_logistic_regression(nrep, thread, output_prefix, output_dir, seq_len,
         df = pd.read_csv(feature_file, sep="\t")
         feature_df = pd.concat([feature_df, df])
 
-    #feature_df = feature_df.drop(columns=['chrom', 'start', 'end', 'sample', 'hap'])
-    feature_df.to_csv(output_dir + '/' + output_prefix + '.all.features', sep="\t", index=False)
+    all_feature_file = output_dir + '/' + output_prefix + '.all.features'
+    model_file = output_dir + '/' + output_prefix + '.logistic.regression.model'
+
+    feature_df.to_csv(all_feature_file, sep="\t", index=False)
+    feature_df = feature_df.drop(columns=['chrom', 'start', 'end', 'sample', 'hap'])
+    train_logistic_regression(feature_df, model_file)
 
 
 def _train_extra_trees():