Merge branch 'unit-tests' into develop

* unit-tests:
  Added test recipe
  Using mock data on unit tests (#1)
  Addded unit test for pre-processing

Makefile

@@ -14,3 +14,6 @@ build:
 
 local-install:
 	$(ENV_PATH)pip install .
+
+test:
+	$(ENV_PATH)pytest

tests/test_discovery_svm.py

@@ -0,0 +1,58 @@
+from pCRscore import discovery_svm
+import pandas as pd
+from unittest import mock
+import pytest
+import numpy as np
+
+@pytest.fixture
+def mock_data():
+  # Define the column names
+  columns = [
+    "Trial", "Mixture", "B.cells.Memory", "B.cells.Naive", "CAFs.MSC.iCAF.like",
+    "CAFs.myCAF.like", "DCs", "Endothelial.ACKR1", "Endothelial.CXCL12",
+    "Endothelial.LYVE1", "Endothelial.RGS5", "GenMod1", "GenMod2", "GenMod3",
+    "GenMod4", "GenMod5", "GenMod6", "GenMod7", "Luminal.Progenitors",
+    "Macrophage", "Mature.Luminal", "Monocyte", "Myoepithelial", "NK.cells",
+    "NKT.cells", "Plasmablasts", "PVL.Differentiated", "PVL.Immature",
+    "T.cells.CD4.", "T.cells.CD8.", "Cancer.Cells", "Normal.Epi", "TCells",
+    "Myeloids", "BCells", "CAFs", "PVLs", "Endothelials", "ER", "Response",
+    "Cohort", "PAM50", "PAM50_Normal", "PAM50_LumA", "PAM50_Her2", "PAM50_LumB",
+    "PAM50_Basal"
+  ]
+
+  # Number of rows you want in your DataFrame
+  num_rows = 100  # Adjust this based on how many rows you need
+
+  # Create a dataframe with random values in (0, 1) for the numerical columns
+  n_numerical_columns = 7
+  df = pd.DataFrame(
+    np.random.rand(num_rows, len(columns) - n_numerical_columns),
+    columns = columns[:-n_numerical_columns]
+  )
+
+  # Adding the non-numerical columns manually
+  df['Trial'] = 'E-MTAB-4439'
+  df['Mixture'] = 'Mixture1'
+  df['Cohort'] = 'Discovery'
+  df['Response'] = np.random.choice(['pCR', 'RD'], num_rows)
+  df['ER'] = np.random.choice(['Positive', 'Negative'], num_rows)
+  df['PAM50'] = np.random.choice(['LumA', 'Basal'], num_rows)
+  df['PAM50_Normal'] = np.random.choice([True, False], num_rows)
+  df['PAM50_LumA'] = np.random.choice([True, False], num_rows)
+  df['PAM50_Her2'] = np.random.choice([True, False], num_rows)
+  df['PAM50_LumB'] = np.random.choice([True, False], num_rows)
+  df['PAM50_Basal'] = np.random.choice([True, False], num_rows)
+
+  return df
+
+@mock.patch('pandas.read_csv')
+def test_preprocess(mock_read_csv, mock_data):
+   # Configure the mock to return your predefined DataFrame
+    mock_read_csv.return_value = mock_data
+
+    data = pd.read_csv("Data NAC cohort _1_.csv") # returns mock data instead
+    data = discovery_svm.preprocess(data)
+    assert data.shape == (100, 48)
+
+    X, y = discovery_svm.extract_features(data)
+    assert X.shape == (100, 44)