✨ Generalisation

Src/Scripts/generalisation.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+# coding: utf-8
+from qiskit.aqua.components.optimizers import COBYLA, ADAM, SPSA
+from qiskit.circuit.library import ZZFeatureMap, RealAmplitudes, ZFeatureMap, PauliFeatureMap
+
+from Benchmarking import Benchmark, normalize_data
+import csv
+
+
+import numpy as np
+import pandas as pd
+
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+from sklearn.utils import shuffle
+
+import csv
+import warnings
+
+warnings.filterwarnings("ignore")
+
+best_models = [
+    {"Featuremap": "ZFeatureMap(4, reps=2)", "Opt": "SPSA(max_trials=50)", "vdepth": 5},
+    {"Featuremap": "ZFeatureMap(4, reps=2)", "Opt": "SPSA(max_trials=50)", "vdepth": 3},
+    {"Featuremap": "ZFeatureMap(4, reps=2)", "Opt": "COBYLA(maxiter=50)", "vdepth": 3},
+    {"Featuremap": "ZFeatureMap(4, reps=2)", "Opt": "SPSA(max_trials=50)", "vdepth": 1},
+    {"Featuremap": "ZFeatureMap(4, reps=1)", "Opt": "COBYLA(maxiter=50)", "vdepth": 1},
+    {"Featuremap": "ZZFeatureMap(4, reps=1)", "Opt": "SPSA(max_trials=50)", "vdepth": 5},
+    {"Featuremap": "ZFeatureMap(4, reps=2)", "Opt": "COBYLA(maxiter=50)", "vdepth": 5},
+    {"Featuremap": "ZFeatureMap(4, reps=1)", "Opt": "SPSA(max_trials=50)", "vdepth": 3},
+    {"Featuremap": "ZFeatureMap(4, reps=1)", "Opt": "SPSA(max_trials=50)", "vdepth": 5},
+    {"Featuremap": "ZFeatureMap(4, reps=1)", "Opt": "COBYLA(maxiter=50)", "vdepth": 3},
+]
+
+def normalize_data(dataPath = "../../Data/Processed/iris_csv.csv"):
+    """
+    Normalizes the data
+    """
+    # Reads the data
+    data = pd.read_csv(dataPath)
+    data = shuffle(data, random_state=42)
+    if dataPath.__contains__("iris"):
+        X, Y = data[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']].values, data['target'].values
+    elif dataPath.__contains__("wine"):
+        X, Y = data[['alcohol', 'flavanoids', 'color_intensity', 'proline', 'target']].values, data['target'].values
+    # normalize the data
+    scaler = MinMaxScaler(feature_range=(-2 * np.pi, 2 * np.pi))
+    X = scaler.fit_transform(X)
+    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42)
+    return X_train, X_test, Y_train, Y_test
+
+def main():
+    data = {}
+    dataPaths = ["../../Data/Processed/iris_csv.csv", "../../Data/Processed/winedata.csv"]
+    for dataPath in dataPaths:
+        x_train, x_test, y_train, y_test = normalize_data(dataPath = dataPath)
+        for model in best_models:
+            print("FE: {}\tDepth: {}\tOpt: {}".format(model['Featuremap'], model['vdepth'], model['Opt']))
+            test_benchmark = Benchmark(optimizer=eval(model['Opt']), variational_depth=model['vdepth'], feature_map=eval(model['Featuremap']), X_train=x_train, X_test=x_test, Y_train=y_train, Y_test=y_test)
+            test_benchmark.run()
+            data_list = "{} {} vdepth {}".format(model['Featuremap'], model['Opt'], model['vdepth'])
+            data[data_list] = test_benchmark.get_cost_list()
+
+        if dataPath.__contains__("iris"):
+            w = csv.writer(open("../../Data/Processed/iriscost1.csv", "w"))
+        else:
+            w = csv.writer(open("../../Data/Processed/winecost1.csv", "w"))
+        for key, val in data.items():
+            w.writerow([key, val])
+
+
+if __name__ == "__main__":
+    main()