Added data with access to electricity to have binary classification

Energy Plants.py

@@ -14,36 +14,48 @@
 from scipy.spatial.distance import pdist
 import geopy.distance
 
-dataset = pd.read_csv('C:/Users/Saad Mufti/Documents/GitHub/Energy-Plant-Locations-Quantum-Optimization/EnergyAccessDataset.csv', encoding='ANSI')
+dataset = pd.read_csv('C:/Users/Saad Mufti/Documents/GitHub/Energy-Plant-Locations-Quantum-Optimization/EnergyAccessSurveyFull.csv', low_memory=False, encoding='ANSI')
 dataset = dataset.set_index('Sr_ no_')
-dataset = dataset.drop_duplicates(subset='GPS')
+dataset = dataset[['GPS', 'ES3']]
+dataset[['Elec_access']] = dataset[['ES3']] != 7 
+without_elec = dataset.loc[dataset['Elec_access'] == False] 
+without_elec = without_elec.drop_duplicates(subset='GPS')
+print(without_elec)
+
+dataset = dataset.drop_duplicates(subset='GPS') 
+# dataset = dataset[['GPS', 'ES3']]
+dataset.append(without_elec)
+
+print('Dataset has', len(dataset))
+
 #TODO: Use bigger dataset instead so can get trues and falses. Then apply algo
-# bools = dataset
+
 # bools[['Elec_access']] = dataset[['ES3']] != 7 
-# bools = list(bools['Elec_access'])
 # print(bools)
+# print(bools.loc[bools['Elec_access'] == False])
+bools = list(without_elec['Elec_access'])
 dataset = dataset[['GPS']]
-
-dataset[['Latitude', 'Longitude']] = dataset['GPS'].str.split(',', expand=True)
-dataset = dataset.drop(['GPS'], axis=1)
-# print(dataset)
-
-distance_matrix = np.array(dataset[['Latitude', 'Longitude']])
+# print(bools)
+dataset_sample = pd.concat([without_elec[['GPS']], dataset.sample(8)])
+print(dataset_sample)
+# dataset_sample = dataset_sample[['GPS']]
+dataset_sample[['Latitude', 'Longitude']] = dataset_sample['GPS'].str.split(',', expand=True)
+dataset_sample = dataset_sample.drop(['GPS'], axis=1)
+print(dataset_sample)
+
+distance_matrix = np.array(dataset_sample[['Latitude', 'Longitude']])
 # print(distance_matrix)
-
-# m_dist = pdist(distance_matrix, lambda u, v: geopy.distance.distance(u, v).kilometers)
-
 # print(m_dist)
 
-dist = pd.DataFrame(distances_dataset(dataset.values, 
-                                    lambda u, v: geopy.distance.distance(u, v).kilometers), index=dataset.index, columns=dataset.index)
+dist = pd.DataFrame(distances_dataset(dataset_sample.values, 
+                                    lambda u, v: geopy.distance.distance(u, v).kilometers), index=dataset_sample.index, columns=dataset_sample.index)
 print(dist)
 # print(dist.iloc[0,:])
 
 hamiltonian = hamiltonian_from_distances(dist)
 timesteps = 3
 iterations = 500
-n_qubits = 7 #10
+n_qubits = 16 #10
 betas = [round(val,1) for val in np.random.rand(timesteps*n_qubits)]
 gammas_singles = [round(val,1) for val in np.random.rand(0)] 
 gammas_pairs = [round(val,1) for val in np.random.rand(timesteps*len(hamiltonian))]
@@ -79,7 +91,7 @@ def run_qaoa(hamiltonian, params, timesteps, max_iters, init_state=None):
 wave_func = cost_function.get_wavefunction(params.raw())
 lowest = max_probability_bitstring(wave_func.probabilities())
 
-true_clusters = [1 if val else 0 for val in labels]
+true_clusters = [1 if val else 0 for val in bools]
 acc = cluster_accuracy(lowest,true_clusters)
 
 print(res)