diff --git a/data_exploration/wellness_cluster_test.py b/data_exploration/wellness_cluster_test.py new file mode 100644 index 0000000..f25c629 --- /dev/null +++ b/data_exploration/wellness_cluster_test.py @@ -0,0 +1,25 @@ +import sklearn.cluster as cluster +import pandas as pd +from matplotlib import pyplot as plt + + +def find_kmeans(mat, k, first, second): + km = cluster.KMeans(n_clusters=k) + km.fit(mat) + + # Plot sse against k + plt.figure(figsize=(6, 6)) + plt.xlabel('Metric: ' + first) + plt.ylabel('Metric: ' + second) + plt.scatter(mat[:, 0], mat[:, 1], c=km.labels_, cmap='rainbow') + plt.show() + + +# Read csv in +df = pd.read_csv('../data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv') + +# Specify what things you want +df = df[["normFatigue", "normSleepQuality"]] + +# values, num clusters, axis labelsg +find_kmeans(df.values, 2, "normFatigue", "normSleepQuality") \ No newline at end of file diff --git a/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv b/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv index df1b22e..54a374d 100644 --- a/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv +++ b/data_preparation/cleaned/expSmoothWorkAndFatigueData.csv @@ -1,4 +1,4 @@ -"","day","totalWork","averageWorkLoad","smoothedWork","smoothedFatigueData" +"","TimeSinceAugFirst","totalWork","averageWorkLoad","smoothedWork","smoothedFatigueData" "1",0,4855,255.526315789474,4855,0.734457489003587 "2",1,1380,138,5921.8864123285,1.17226558337237 "3",2,6030,231.923076923077,11569.9661032971,1.37713723445689 diff --git a/data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv b/data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv new file mode 100644 index 0000000..452d382 --- /dev/null +++ b/data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv @@ -0,0 +1,5012 @@ +,Unnamed: 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a/data_preparation/cleaned/time_series_notnormalized_with_0Nan_rpe.csv b/data_preparation/cleaned/time_series_notnormalized_with_0Nan_rpe.csv index c2a075e..801ada8 100644 --- a/data_preparation/cleaned/time_series_notnormalized_with_0Nan_rpe.csv +++ b/data_preparation/cleaned/time_series_notnormalized_with_0Nan_rpe.csv @@ -36,8 +36,8 @@ 34,34,34,2018-07-17,4,1,Skills,60.0,5.0,300.0,300.0,107.1,309.11,0.35,7.0,8.0,,0,0,1,0,0,0,0,0,0,0,0,1,350 35,35,35,2018-07-17,5,1,Skills,60.0,5.0,300.0,300.0,42.9,100.71,0.43,9.0,9.0,,0,0,1,0,0,0,0,0,0,0,0,1,350 36,36,36,2018-07-17,6,1,Speed,30.0,3.0,90.0,370.0,52.9,333.5,0.16,0.0,0.0,,0,0,0,0,0,0,1,0,0,0,0,1,350 -37,37,37,2018-07-17,6,1,Conditioning,35.0,8.0,280.0,0.0,0.0,0.0,0.0,0.0,0.0,,0,0,0,1,0,0,0,0,0,0,0,1,350 38,38,38,2018-07-17,7,1,Skills,75.0,6.0,450.0,450.0,205.7,401.79,0.51,0.0,0.0,,0,0,1,0,0,0,0,0,0,0,0,1,350 +37,37,37,2018-07-17,6,1,Conditioning,35.0,8.0,280.0,0.0,0.0,0.0,0.0,0.0,0.0,,0,0,0,1,0,0,0,0,0,0,0,1,350 39,39,39,2018-07-17,10,1,Skills,60.0,4.0,240.0,240.0,79.3,298.57,0.27,0.0,0.0,,0,0,1,0,0,0,0,0,0,0,0,1,350 40,40,40,2018-07-17,11,1,Skills,90.0,5.0,450.0,450.0,210.0,391.36,0.54,9.0,9.0,Absolutely,0,0,1,0,0,0,0,0,0,1,0,0,350 41,41,41,2018-07-17,13,1,Skills,90.0,6.0,540.0,540.0,268.6,309.25,0.87,0.0,0.0,Not at all,0,0,1,0,0,0,0,0,1,0,0,0,350 diff --git a/data_preparation/time_series.py b/data_preparation/time_series.py index 193bbf6..f7462a0 100644 --- a/data_preparation/time_series.py +++ b/data_preparation/time_series.py @@ -30,4 +30,4 @@ def normalize_time_series(path, filename, start): start = start_end_times("data/rpe.csv") -normalize_time_series("data/games_ranked.csv", "games_ranked.csv", start) +normalize_time_series("cleaned/normalizedWellness.csv", "normalizedWellness.csv", start) diff --git a/findings/data_plot.py b/findings/data_plot.py new file mode 100644 index 0000000..af9fb25 --- /dev/null +++ b/findings/data_plot.py @@ -0,0 +1,13 @@ +from matplotlib import pyplot as plt +import pandas as pd + + +def plot_xy(x, y): + plt.scatter(x, y) + plt.xlabel(x.name) + plt.ylabel(y.name) + plt.show() + + +wellness = pd.read_csv('../data_preparation/data/wellness.csv') +plot_xy(wellness['Fatigue'], wellness['SleepHours']) diff --git a/findings/r2correlation.txt b/findings/r2correlation.txt new file mode 100644 index 0000000..e69de29 diff --git a/hypotheses_modeling/21DaySlidingWorkAverage.csv b/hypotheses_modeling/21DaySlidingWorkAverage.csv new file mode 100644 index 0000000..21a6be5 --- /dev/null +++ b/hypotheses_modeling/21DaySlidingWorkAverage.csv @@ -0,0 +1,336 @@ +"","TimeSinceAugFirst","slidingWorkAverage" +"1",20,5563.95 +"2",21,5895.66666666667 +"3",22,6307.42857142857 +"4",23,6331.71428571429 +"5",24,6458.52380952381 +"6",25,6752.33333333333 +"7",26,6767.33333333333 +"8",27,6766.61904761905 +"9",28,6738.42857142857 +"10",29,6798.66666666667 +"11",30,6811.38095238095 +"12",31,6813.7619047619 +"13",32,6881.85714285714 +"14",33,6840.42857142857 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+def time_series_sigmoid_classification(X, Y, k, n0, x_columns, y_columns): + inp = X[x_columns] + out = Y[y_columns] + col = "day" + x = [] + y = [] + input_shape = 0 + output_shape = 0 + for player in Y["playerID"].unique(): + XPlayer = inp[inp["playerID"] == player] + YPlayer = out[out["playerID"] == player] + for day in YPlayer[col][n0 - 1:]: + prev = day - k + xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy() + if xprev.shape[0] != 1: + continue + else: + xprev = xprev[0, :] + yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :] + if input_shape == 0: + input_shape = xprev.shape[0] + else: + if input_shape != xprev.shape[0]: + print("INCONSISTENT INPUT DIMENSION") + exit(2) + if input_shape == 0: + output_shape = yt.shape[0] + else: + if output_shape != yt.shape[0]: + print("INCONSISTENT OUTPUT DIMENSION") + exit(2) + x.append(xprev) + y.append(yt) + x = np.array(x) + y = np.array(y) + model = tf.keras.Sequential([ + tf.keras.layers.Flatten(input_shape=input_shape), + tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax) + ]) + model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) + model.fit(x, y, epochs=100) + loss, accuracy = model.evaluate(x, y) + print(loss, accuracy) + return model.get_weights() + + +def time_series_dnn_classification(X, Y, k, n0, x_columns, y_columns): + inp = X[x_columns] + out = Y[y_columns] + col = "day" + x = [] + y = [] + input_shape = 0 + output_shape = 0 + for player in Y["playerID"].unique(): + XPlayer = inp[inp["playerID"] == player] + YPlayer = out[out["playerID"] == player] + for day in YPlayer[col][n0 - 1:]: + prev = day - k + xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy() + if xprev.shape[0] != 1: + continue + else: + xprev = xprev[0, :] + yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :] + if input_shape == 0: + input_shape = xprev.shape[0] + else: + if input_shape != xprev.shape[0]: + print("INCONSISTENT INPUT DIMENSION") + exit(2) + if input_shape == 0: + output_shape = yt.shape[0] + else: + if output_shape != yt.shape[0]: + print("INCONSISTENT OUTPUT DIMENSION") + exit(2) + x.append(xprev) + y.append(yt) + x = np.array(x) + y = np.array(y) + model = tf.keras.Sequential([ + tf.keras.layers.Flatten(input_shape=input_shape), + tf.keras.layers.Dense(32, activation=tf.nn.softmax), + tf.keras.layers.Dense(output_shape, activation=tf.nn.softmax) + ]) + model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) + model.fit(x, y, epochs=100) + loss, accuracy = model.evaluate(x, y) + print(loss, accuracy) + return model.get_weights() + +def time_series_linear_regression(X, Y, k, n0, x_columns, y_columns): + inp = X[x_columns] + out = Y[y_columns] + col = "day" + x = [] + y = [] + input_shape = 0 + output_shape = 0 + for player in Y["playerID"].unique(): + XPlayer = inp[inp["playerID"] == player] + YPlayer = out[out["playerID"] == player] + for day in YPlayer[col][n0 - 1:]: + prev = day - k + xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy() + if xprev.shape[0] != 1: + continue + else: + xprev = xprev[0, :] + yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :] + if input_shape == 0: + input_shape = xprev.shape[0] + else: + if input_shape != xprev.shape[0]: + print("INCONSISTENT INPUT DIMENSION") + exit(2) + if input_shape == 0: + output_shape = yt.shape[0] + else: + if output_shape != yt.shape[0]: + print("INCONSISTENT OUTPUT DIMENSION") + exit(2) + x.append(xprev) + y.append(yt) + x = np.array(x) + y = np.array(y) + model = tf.keras.Sequential([ + tf.keras.layers.Flatten(input_shape=input_shape), + tf.keras.layers.Dense(output_shape) + ]) + model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) + model.fit(x, y, epochs=100) + loss, accuracy = model.evaluate(x, y) + print(loss, accuracy) + return model.get_weights() + + +def time_series_dnn_regressions(X, Y, k, n0, x_columns, y_columns): + inp = X[x_columns] + out = Y[y_columns] + col = "day" + x = [] + y = [] + input_shape = 0 + output_shape = 0 + for player in Y["playerID"].unique(): + XPlayer = inp[inp["playerID"] == player] + YPlayer = out[out["playerID"] == player] + for day in YPlayer[col][n0 - 1:]: + prev = day - k + xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col]).to_numpy() + if xprev.shape[0] != 1: + continue + else: + xprev = xprev[0, :] + yt = YPlayer[YPlayer[col] == day].drop(columns=[col]).to_numpy()[0, :] + if input_shape == 0: + input_shape = xprev.shape[0] + else: + if input_shape != xprev.shape[0]: + print("INCONSISTENT INPUT DIMENSION") + exit(2) + if input_shape == 0: + output_shape = yt.shape[0] + else: + if output_shape != yt.shape[0]: + print("INCONSISTENT OUTPUT DIMENSION") + exit(2) + x.append(xprev) + y.append(yt) + x = np.array(x) + y = np.array(y) + model = tf.keras.Sequential([ + tf.keras.layers.Flatten(input_shape=input_shape), + tf.keras.layers.Dense(32, activation=tf.nn.softmax), + tf.keras.layers.Dense(output_shape) + ]) + model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy']) + model.fit(x, y, epochs=100) + loss, accuracy = model.evaluate(x, y) + print(loss, accuracy) + return model.get_weights() diff --git a/hypotheses_modeling/fatigue_total_sum.csv b/hypotheses_modeling/fatigue_total_sum.csv new file mode 100644 index 0000000..121bd97 --- /dev/null +++ b/hypotheses_modeling/fatigue_total_sum.csv @@ -0,0 +1,355 @@ +TimeSinceAugFirst,fatigueSum +0,0.734457489003587 +1,0.485175472200728 +2,0.280474592899874 +3,0.852899672028512 +4,0.067718127997734 +5,0.387261248267282 +6,0.466815138124766 +7,0.763731947890889 +8,0.685512083480377 +9,0.895108656027463 +10,-0.061861871122328 +11,0.296321907390904 +12,0.623117762114496 +13,0.753829250795893 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b/hypotheses_modeling/hypotheses.txt new file mode 100644 index 0000000..a95b2b3 --- /dev/null +++ b/hypotheses_modeling/hypotheses.txt @@ -0,0 +1,28 @@ +Team: + 1. + x - normFatigue, TimeSince + y - normSoreness + r2 - 0.16830803694995466 + + 2. + x - normFatigue, TimeSince + y - normDesire + r2 - 0.1722466278587138 + + 3. + x - normFatigue, TimeSince + y - normIrritability + r2 - 0.09563459728506452 + + 4. + x - normSleepHours, TimeSince + y - normSleepQualiyt + r2 - 0.09717095676693188 + + 5. + +Perry: +7 day moving average team workload - normalized team fatigue: 0.0006 +21 day moving average team workload - normalized team fatigue: 0.0024 +normalized team fatigue - game day performance: 0.0696 +normalized team fatigue - paper smoothed workload fatigue: 0.0324 diff --git a/hypotheses_modeling/slidingWorkAverageSevenDay.csv b/hypotheses_modeling/slidingWorkAverageSevenDay.csv new file mode 100644 index 0000000..31f6d20 --- /dev/null +++ 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+ prev = day - k + xprev = X[X[col] == prev].drop(columns=[col]).to_numpy() + if xprev.shape[0] != 1: + continue + else: + xprev = xprev[0, :] + yt = y[y[col] == day].drop(columns=[col]).to_numpy()[0, :] + inp.append(xprev) + out.append(yt) + regr = linear_model.LinearRegression() + regr.fit(inp, out) + predictions = regr.predict(inp) + mse = mean_squared_error(out, predictions)/(len(out) - 2) + r2 = r2_score(out, predictions) + return regr.intercept_, regr.coef_, r2, mse + + +def standard_lr(x, y): + regr = linear_model.LinearRegression() + regr.fit(x, y) + predictions = regr.predict(x) + mse = mean_squared_error(y, predictions) / (len(y) - 2) + r2 = r2_score(y, predictions) + return regr.intercept_, regr.coef_, r2, mse + + +def main(): + # fatigueSums = pd.read_csv("fatigue_total_sum.csv") + # workMovingAverage21 = pd.read_csv("21DaySlidingWorkAverage.csv", index_col=0) + # print(k_days_into_future_regression(workMovingAverage21, fatigueSums, 0, 21)) + + + wellness = pd.read_csv("../data_preparation/cleaned/time_series_normalized_wellness_menstruation.csv") + + wellness = wellness.fillna(0) + x = wellness[['normSoreness', 'TimeSinceAugFirst']] + y = wellness['normFatigue'] + print(wellness.isnull().sum()) + print(standard_lr(x, y)) + + +if __name__ == "__main__": + main() diff --git a/hypotheses_modeling/time_series_days_ranked.csv b/hypotheses_modeling/time_series_days_ranked.csv new file mode 100644 index 0000000..b64b2f7 --- /dev/null +++ b/hypotheses_modeling/time_series_days_ranked.csv @@ -0,0 +1,18 @@ +,TimeSinceAugFirst,DailyElo +0,121,0.0 +1,122,-3.714599999999998 +2,178,0.04346000000000028 +3,179,2.1916710000000013 +4,180,0.0 +5,255,0.0 +6,256,0.0 +7,257,-2.520374784999996 +8,263,-2.0880156214999985 +9,264,-1.7032140593500005 +10,284,-0.6130256153877235 +11,285,2.620463284090865 +12,311,-2.076954630427971 +13,312,1.0960590427574828 +14,313,1.8954531384817344 +15,353,-0.2940921753664384 +16,354,-1.8646829578297937