Added input casting to fit_syndna_models

pysyndna/src/fit_syndna_models.py

@@ -10,7 +10,7 @@
 
 from typing import Optional, List, Dict, Union
 from pysyndna.src.util import validate_required_columns_exist, \
-    validate_metadata_vs_reads_id_consistency, SAMPLE_ID_KEY
+    validate_metadata_vs_reads_id_consistency, cast_cols, SAMPLE_ID_KEY
 
 DEFAULT_MIN_SAMPLE_COUNTS = 1
 
@@ -401,6 +401,18 @@ def fit_linear_regression_models(
     validate_required_columns_exist(
         sample_syndna_weights_and_total_reads_df, expected_info_cols,
         "sample metadata is missing required column(s)")
+    expected_syndna_cols = [SYNDNA_ID_KEY, SYNDNA_INDIV_NG_UL_KEY]
+    validate_required_columns_exist(
+        syndna_concs_df, expected_syndna_cols,
+        "syndna concentrations are missing required column(s)")
+
+    # cast numeric input columns to the correct type
+    sample_syndna_weights_and_total_reads_df = cast_cols(
+        sample_syndna_weights_and_total_reads_df, [SYNDNA_POOL_MASS_NG_KEY])
+    sample_syndna_weights_and_total_reads_df = cast_cols(
+        sample_syndna_weights_and_total_reads_df,
+        [SAMPLE_TOTAL_READS_KEY], int)
+    syndna_concs_df = cast_cols(syndna_concs_df, [SYNDNA_INDIV_NG_UL_KEY])
 
     # id any syndnas that have an inadequate total number of reads aligned
     # to them across all samples (less than min_sample_counts). Don't drop yet.

pysyndna/tests/test_fit_syndna_models.py

@@ -180,6 +180,45 @@ def test_fit_linear_regression_models_for_qiita(self):
 
         self.assertDictEqual(expected_out, output_dict)
 
+    def test_fit_linear_regression_models_for_qiita_w_casts(self):
+        # same as test_fit_linear_regression_models_for_qiita, but with
+        # all param values passed in as strings
+        prep_info_dict = {k: [str(x) for x in self.prep_info_dict[k]]
+                          for k in self.prep_info_dict}
+        prep_info_df = pd.DataFrame(prep_info_dict)
+        input_biom = biom.table.Table(
+            self.reads_per_syndna_per_sample_array,
+            self.reads_per_syndna_per_sample_dict[SYNDNA_ID_KEY],
+            self.sample_ids)
+        min_counts = 50
+
+        # These are text versions of the linear regression results
+        # for the full data (see self.lingress_results and the
+        # "linear regressions" sheet of "absolute_quant_example.xlsx").
+        expected_out = {
+            'lin_regress_by_sample_id':
+                'A:\n'
+                '  intercept: -6.724238188489\n'
+                '  intercept_stderr: 0.236197627825\n'
+                '  pvalue: 1.42844e-07\n'
+                '  rvalue: 0.986503097515\n'
+                '  slope: 1.244876523791\n'
+                '  stderr: 0.073054085503\n'
+                'B:\n'
+                '  intercept: -7.155318973708\n'
+                '  intercept_stderr: 0.256395675584\n'
+                '  pvalue: 1.50538e-07\n'
+                '  rvalue: 0.986324179735\n'
+                '  slope: 1.246759136044\n'
+                '  stderr: 0.073657952553\n',
+            'fit_syndna_models_log': ''
+        }
+
+        output_dict = fit_linear_regression_models_for_qiita(
+            prep_info_df, input_biom, min_counts)
+
+        self.assertDictEqual(expected_out, output_dict)
+
     def test_fit_linear_regression_models_for_qiita_w_alt_config(self):
         prep_info_df = pd.DataFrame(self.prep_info_dict)
         input_biom = biom.table.Table(
@@ -316,6 +355,34 @@ def test_fit_linear_regression_models(self):
             self.lingress_results, out_linregress_dict)
         self.assertEqual([], out_msgs)
 
+    def test_fit_linear_regression_models_w_casts(self):
+        min_count = 50
+
+        # same as test_fit_linear_regression_models, but with
+        # all param values passed in as strings
+        syndna_concs_dict = {k: [str(x) for x in self.syndna_concs_dict[k]]
+                            for k in self.syndna_concs_dict}
+        syndna_concs_df = pd.DataFrame(syndna_concs_dict)
+        a_b_sample_syndna_weights_and_total_reads_dict = {
+            k: [str(x) for x in
+                self.a_b_sample_syndna_weights_and_total_reads_dict[k]]
+            for k in self.a_b_sample_syndna_weights_and_total_reads_dict}
+        sample_syndna_weights_and_total_reads_df = pd.DataFrame(
+            a_b_sample_syndna_weights_and_total_reads_dict)
+
+        reads_per_syndna_per_sample_df = pd.DataFrame(
+            self.reads_per_syndna_per_sample_dict)
+        reads_per_syndna_per_sample_df.set_index(SYNDNA_ID_KEY, inplace=True)
+
+        out_linregress_dict, out_msgs = fit_linear_regression_models(
+            syndna_concs_df,
+            sample_syndna_weights_and_total_reads_df,
+            reads_per_syndna_per_sample_df, min_count)
+
+        self.assert_lingressresult_dict_almost_equal(
+            self.lingress_results, out_linregress_dict)
+        self.assertEqual([], out_msgs)
+
     def test_fit_linear_regression_models_w_log_msgs(self):
         min_count = 200