deleted unnecessary tests

test/test_Prediction.py

-import TDA_Prediction as tdap
+from Topological_ML import TDA_Prediction as tdap
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import kmapper as km
 import pandas as pd
-from unittest.mock import patch, Mock
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn
+from sklearn import ensemble
 
-
-def test_numpy_to_pandas():
-    from sklearn.datasets import fetch_california_housing
-    cal_housing = fetch_california_housing()
-    value = tdap.numpy_to_pandas(cal_housing)
-    assert value == df
+def test_data_summary():
+    data = pd.DataFrame({"A": [1,2,3,4,5,6,7,8,9,10]})
+    correct_response = {"head": [1,2,3,4,5,6,7,8,9,10], "shape": [10, 1], "describe": 0}
+    values = tdap.data_summary(data, 5)
+    assert values == correct_response
     return
 
-def test_accuracy_metrics1():
-    values = tdap.accuracy_metrics(None, None)
-    assert values == None
-    return 
+def test_linear_regression():
+    a = pd.DataFrame({"A": [1,2,3,4,5,6,7,8,9,10]})
+    b = pd.DataFrame({"B": [2,4,6,8,10,12,14,16,18,20]})
+    correct_response = 1
+    values = tdap.linear_regression(a, b)
+    assert values == correct_response
+    return
 
-def test_accuracy_metrics2():
-    values = tdap.accuracy_metrics({}, None)
-    assert values == None
-    return 
+def test_lens_1d():
+    a = pd.DataFrame({"A": [0,0]})
+    correct_response = np.array([[0., 0.],[0., 0.]])
+    values = tdap.lens_1d(a,123,1)
+    assert values == correct_response
+    return
 
-def test_descriptive_statistic():
-    data = pd.DataFrame({'a':[1,2,3,4,5]})
-    values = tdap.descriptive_statistic(data, 5)
-    assert values == 5
-    return 
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