ML logs

2022-06-20 21:55:47 +01:00 · 2022-06-20 21:55:47 +01:00 · cadaed2ba7
commit cadaed2ba7
parent 4c5afa614f
35 changed files with 568790 additions and 0 deletions
--- a/scripts/ml/log_alr_cd_7030.txt
+++ b/scripts/ml/log_alr_cd_7030.txt
@ -0,0 +1,113 @@
+/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_cd_7030.py:548: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+  mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
+1.22.4
+1.4.1
+
+aaindex_df contains non-numerical data
+
+Total no. of non-numerial columns: 2
+
+Selecting numerical data only
+
+PASS: successfully selected numerical columns only for aaindex_df
+
+Now checking for NA in the remaining aaindex_cols
+
+Counting aaindex_df cols with NA 
+ncols with NA: 4 columns 
+Dropping these... 
+Original ncols: 127
+
+Revised df ncols: 123
+
+Checking NA in revised df...
+
+PASS: cols with NA successfully dropped from aaindex_df 
+Proceeding with combining aa_df with other features_df
+
+PASS: ncols match 
+Expected ncols: 123 
+Got: 123
+
+Total no. of columns in clean aa_df: 123
+
+Proceeding to merge, expected nrows in merged_df: 271
+
+PASS: my_features_df and aa_df successfully combined 
+nrows: 271 
+ncols: 269
+count of NULL values before imputation
+
+or_mychisq          256
+log10_or_mychisq    256
+dtype: int64
+count of NULL values AFTER imputation
+
+mutationinformation    0
+or_rawI                0
+logorI                 0
+dtype: int64
+
+PASS: OR values imputed, data ready for ML
+
+Total no. of features for aaindex: 123
+
+No. of numerical features: 168 
+No. of categorical features: 7
+
+PASS: x_features has no target variable
+
+No. of columns for x_features: 175
+
+------------------------------------------------------------- 
+Successfully split data with stratification [COMPLETE data]: 70/30 
+Original data size: (271, 175) 
+Train data size: (181, 175) 
+Test data size: (90, 175) 
+y_train numbers: Counter({0: 180, 1: 1}) 
+y_train ratio: 180.0 
+ 
+y_test_numbers: Counter({0: 89, 1: 1}) 
+y_test ratio: 89.0 
+-------------------------------------------------------------
+
+index: 0 
+ind: 1
+
+Mask count check: True
+
+index: 1 
+ind: 2
+
+Mask count check: True
+Original Data
+ Counter({0: 180, 1: 1}) Data dim: (181, 175)
+
+Simple Random OverSampling
+ Counter({0: 180, 1: 180})
+(360, 175)
+
+Simple Random UnderSampling
+ Counter({0: 1, 1: 1})
+(2, 175)
+
+Simple Combined Over and UnderSampling
+ Counter({0: 180, 1: 180})
+(360, 175)
+Traceback (most recent call last):
+  File "/home/tanu/git/LSHTM_analysis/scripts/ml/./alr_cd_7030.py", line 19, in <module>
+    setvars(gene,drug)
+  File "/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_cd_7030.py", line 745, in setvars
+    X_smnc, y_smnc = sm_nc.fit_resample(X, y)
+  File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/imblearn/base.py", line 83, in fit_resample
+    output = self._fit_resample(X, y)
+  File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/imblearn/over_sampling/_smote/base.py", line 533, in _fit_resample
+    X_resampled, y_resampled = super()._fit_resample(X_encoded, y)
+  File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/imblearn/over_sampling/_smote/base.py", line 324, in _fit_resample
+    nns = self.nn_k_.kneighbors(X_class, return_distance=False)[:, 1:]
+  File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neighbors/_base.py", line 749, in kneighbors
+    raise ValueError(
+ValueError: Expected n_neighbors <= n_samples,  but n_samples = 1, n_neighbors = 6