Add Activity Recognizer for Python and own gitignore
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Lukas Koeping
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code/lukas/ActivityRecognition_Python/Main.py
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code/lukas/ActivityRecognition_Python/Main.py
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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np.set_printoptions(formatter={'float_kind': '{:8f}'.format})
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from DataReader import load_all_data
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"""
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Data Preprocessing
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"""
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# load the data using DataReader
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data = load_all_data()
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# In the following I use pandas to structure the data for processing
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# Split data regarding activities
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standing_df = data[data['event'] == 'STAND']
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stairs_up_df = data[data['event'] == 'STAIRS_UP']
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stairs_down_df = data[data['event'] == 'STAIRS_DOWN']
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walk_normal_df = data[data['event'] == 'WALK_NORMAL']
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elevator_up_df = data[data['event'] == 'ELEVATOR_UP']
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elevator_down_df = data[data['event'] == 'ELEVATOR_DOWN']
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# Get the biggest timestamp
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maxtime = data.iloc[-1].t
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# Split data in sliding windows
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INTERVAL_SIZE = 1000
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# define start and end times for each interval and remove the first second
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# due to bad data during recording
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start = np.arange(1000, maxtime - INTERVAL_SIZE,
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INTERVAL_SIZE / 2)
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end = np.arange(1000 + INTERVAL_SIZE, maxtime, INTERVAL_SIZE / 2)
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# Pairs of start and end times of every interval
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start_end = np.vstack((start, end)).T
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def extract_intervals(activity_df, start_end):
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intervals = []
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for ii in start_end:
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df = activity_df[(activity_df['t'] > ii[0]) &
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(activity_df['t'] <= ii[1])]
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# only include the dataframe if it contains values
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if df.size > 0:
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intervals.append(df)
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return intervals
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stand_intervals = extract_intervals(standing_df, start_end)
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stairs_up_intervals = extract_intervals(stairs_up_df, start_end)
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stairs_down_intervals = extract_intervals(stairs_down_df, start_end)
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walk_normal_intervals = extract_intervals(walk_normal_df, start_end)
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elevator_up_intervals = extract_intervals(elevator_up_df, start_end)
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elevator_down_intervals = extract_intervals(elevator_down_df, start_end)
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# Calculate features
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# from feature_calculator import calculate_features
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def calculate_features(intervals):
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feature_df = pd.DataFrame(
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columns=["ax_mean", "ay_mean", "az_mean", "bx_mean",
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"ax_var", "ay_var", "az_var", "bx_var",
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'mag_mean', 'mag_var'])
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all_ax = np.ndarray(len(intervals))
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for ind, interval in enumerate(intervals):
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ax = interval['ax'].values # Remove nan from the data
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ax = ax[~np.isnan(ax)]
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ay = interval['ay'].values
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ay = ay[~np.isnan(ay)]
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az = interval['az'].values
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az = az[~np.isnan(az)]
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bx = interval['bx'].values
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bx = bx[~np.isnan(bx)]
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# subtract gravity constant from magnitude
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magnitude = (interval['magnitude'].values) - 9.81
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magnitude = magnitude[~np.isnan(magnitude)]
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if len(bx) == 0:
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continue
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#!!!!!!!BX mean is dependent on the location and the day!!!!!Do not use as feature
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df = pd.DataFrame({'ax_mean': [np.mean(ax)],
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'ay_mean': [np.mean(ay)],
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'az_mean': [np.mean(az)],
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'ax_var': [np.var(ax)],
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'ay_var': [np.var(ay)],
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'az_var': [np.var(az)],
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'bx_var': [np.var(bx)],
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'bx_diff': [bx[0] - bx[-1]],
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'mag_mean': [np.mean(magnitude)],
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'mag_var': [np.var(magnitude)],
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'mag_max': [np.max(magnitude)],
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'mag_min': [np.min(magnitude)],
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'mag_diff': [np.max(magnitude) - np.min(magnitude)]
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})
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# skip dataframe if it contains nan
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if df.isnull().any().any():
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continue
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feature_df = feature_df.append(df)
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return feature_df
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print("Calculating features...")
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features_stand = calculate_features(stand_intervals)
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features_stairs_up = calculate_features(stairs_up_intervals)
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features_stairs_down = calculate_features(stairs_down_intervals)
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features_walk_normal = calculate_features(walk_normal_intervals)
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features_elevator_up = calculate_features(elevator_up_intervals)
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features_elevator_down = calculate_features(elevator_down_intervals)
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# Manually select some features
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features = ["mag_var", "bx_diff"]
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features_stand = features_stand[features]
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features_stairs_up = features_stairs_up[features]
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features_stairs_down = features_stairs_down[features]
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features_walk_normal = features_walk_normal[features]
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features_elevator_up = features_elevator_up[features]
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features_elevator_down = features_elevator_down[features]
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# Create labels
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label_stand = np.full((len(features_stand), ), 1, dtype=np.int)
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label_stairs_up = np.full((len(features_stairs_up), ), 2, dtype=np.int)
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label_stairs_down = np.full((len(features_stairs_down), ), 3, dtype=np.int)
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label_walk_normal = np.full((len(features_walk_normal), ), 4, dtype=np.int)
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label_elevator_up = np.full((len(features_elevator_up), ), 5, dtype=np.int)
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label_elevator_down = np.full((len(features_elevator_down), ), 6, dtype=np.int)
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# Gather all feature vectors and labels in one array
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features = np.vstack((features_stand, features_stairs_up,
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features_stairs_down, features_walk_normal,
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features_elevator_up, features_elevator_down))
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# Scale features
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from sklearn import preprocessing
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features_scaled = preprocessing.scale(features)
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labels = np.hstack((label_stand, label_stairs_up,
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label_stairs_down, label_walk_normal,
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label_elevator_up, label_elevator_down))
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# Use PCA for dimesionality reduction (if necessary)
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from sklearn.decomposition import PCA
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pca = PCA(n_components=2)
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features_trans = pca.fit(features_scaled).transform(features_scaled)
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# Do classification
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# Split in training and testing dataset
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from sklearn import cross_validation
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X_train, X_test, y_train, y_test = cross_validation.train_test_split(
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features, labels, test_size=0.5)
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# Train classifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.svm import SVC
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from sklearn import tree
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# clf = KNeighborsClassifier(n_neighbors=3)
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# clf.fit(X_train, y_train)
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clf = GaussianNB()
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clf.fit(X_train, y_train)
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# clf = SVC()
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# clf.fit(X_train, y_train)
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# clf = tree.DecisionTreeClassifier()
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# clf.fit(X_train, y_train)
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predicts = clf.predict(X_test)
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# Some metrics
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from sklearn.metrics import accuracy_score, classification_report, f1_score, confusion_matrix
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print("Accuracy score: ", accuracy_score(y_test, predicts))
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print("F1 score: ", f1_score(y_test, predicts, average=None))
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print("Classification report: \n", classification_report(y_test, predicts))
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print("Confusion matrix: \n", confusion_matrix(y_test, predicts))
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# Plot some data
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# wähle Merkmal
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# für jede Klasse plotte ein histogram:
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#
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# f = features_scaled[:, 2] # feature 0
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# target_names = ["standing", "stairs_up", "stairs_down",
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# "walk", "elevator_up", "elevator_down"]
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# colors = ['b', 'g', 'r', 'c', 'm', 'y']
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# for label, color, target in zip(range(1, 7), colors, target_names):
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# #sns.kdeplot(f[labels == label])
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# plt.scatter(features_scaled[labels == label, 1], features_scaled[labels == label, 2],
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# label=target, c=color, s=30)
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# plt.legend()
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# fig, ax = plt.subplots(nrows=2, ncols=1)
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# ax[0].scatter(features[labels == 1, 0], features[
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# labels == 1, 1], s=40, c="green", label="standing")
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# ax[0].scatter(features[labels == 2, 0], features[
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# labels == 2, 1], s=40, c="blue", label="stairs_up")
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# ax[0].scatter(features[labels == 3, 0], features[
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# labels == 3, 1], s=40, c="black", label="stairs_down")
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# ax[0].scatter(features[labels == 4, 0], features[
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# labels == 4, 1], s=40, c="yellow", label="walk_normal")
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# ax[0].legend()
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# ax[0].set_xlabel("Mag Mean")
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# ax[0].set_ylabel("Mag_Var")
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# ax[1].scatter(features_trans[labels == 1, 0], features_trans[
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# labels == 1, 1], s=40, c="green", label="standing")
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# ax[1].scatter(features_trans[labels == 2, 0], features_trans[
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# labels == 2, 1], s=40, c="blue", label="stairs_up")
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# ax[1].scatter(features_trans[labels == 3, 0], features_trans[
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# labels == 3, 1], s=40, c="black", label="stairs_down")
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# ax[1].scatter(features_trans[labels == 4, 0], features_trans[
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# labels == 4, 1], s=40, c="yellow", label="walk_normal")
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# ax[1].legend()
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# ax[1].set_xlabel("First principial component")
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# ax[1].set_ylabel("Second principal component")
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