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motifAnalysis.py
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motifAnalysis.py
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matrixprofile import *
sns.set(
rc={"figure.figsize": (18, 6)},
)
sns.set_theme(style="whitegrid")
def motifsanalysis2(ts, w, centroid_name):
ts.plot()
plt.title(centroid_name)
plt.show()
# build matrix profile
mp, mpi = matrixProfile.stomp(ts.values, w)
# print(min(mp))
# print(max(mp))
# print(np.where( mp==max(mp)))
# print(mp)
# print(mpi)
x_coordinates = [np.where(mp == max(mp)), np.where(mp == min(mp))]
y_coordinates = [max(mp), min(mp)]
plt.title("Matrix Profile Centroid" + centroid_name)
plt.plot(mp)
# plt.scatter(x_coordinates, y_coordinates, c="Black")
# plt.axhline(max(mp), color="Red", linestyle ="--", markersize=2)
# plt.axhline(min(mp), color="Red", linestyle ="--", markersize=2)
plt.show()
# motif discovery
mo, mod = motifs.motifs(
ts.values, (mp, mpi), max_motifs=1, ex_zone=0, radius=2, n_neighbors=2
)
print(mo)
print(mod)
flag = True
plt.plot(ts.values)
colors = ["r", "g", "k", "b", "y"][: len(mo)]
for m, d, c in zip(mo, mod, colors):
for i in m:
m_shape = ts.values[i : i + w]
plt.plot(range(i, i + w), m_shape, color=c, lw=3)
plt.title(centroid_name + " with Top-Motif")
plt.show()
def motifsanalysis(ts, w, centroid_name, motif_df):
ts.plot()
plt.title(centroid_name)
plt.show()
# build matrix profile
mp, mpi = matrixProfile.stomp(ts.values, w)
# print(min(mp))
# print(max(mp))
# print(np.where( mp==max(mp)))
# print(mp)
# print(mpi)
x_coordinates = [np.where(mp == max(mp)), np.where(mp == min(mp))]
y_coordinates = [max(mp), min(mp)]
plt.title("Matrix Profile Centroid" + centroid_name)
plt.plot(mp)
# plt.scatter(x_coordinates, y_coordinates, c="Black")
# plt.axhline(max(mp), color="Red", linestyle ="--", markersize=2)
# plt.axhline(min(mp), color="Red", linestyle ="--", markersize=2)
plt.show()
# motif discovery
mo, mod = motifs.motifs(ts.values, (mp, mpi), ex_zone=0, radius=2, n_neighbors=2)
print(mo)
print(mod)
flag = True
plt.plot(ts.values)
colors = ["r", "g", "k", "b", "y"][: len(mo)]
for m, d, c in zip(mo, mod, colors):
for i in m:
# print("Starting point:", i)
# print("Motif: ", ts.values[i:i + w])
# print("Distance: ", d)
row = pd.Series(ts.values[i : i + w]).append(pd.Series(i, index=[15]))
row = row.append(pd.Series(d, index=[16]))
row = row.append(pd.Series(centroid_name, index=[17]))
motif_df = motif_df.append(row, ignore_index=True)
m_shape = ts.values[i : i + w]
plt.plot(range(i, i + w), m_shape, color=c, lw=3)
plt.title(centroid_name + " with Top-Motif")
plt.show()
return motif_df
if __name__ == "__main__":
motif_df = pd.DataFrame()
# read dataset
centroids = pd.read_csv("centroidiclusters_dtw.csv")
print(centroids.info())
print(centroids)
centroids.T.plot()
plt.title("Centroids")
plt.show()
sns.set()
fig, axs = plt.subplots(4, 2, figsize=(10, 12))
axs[0, 0].plot(centroids.iloc[0], color="blue")
axs[0, 0].set_title("Cluster 0")
axs[0, 0].set(xticklabels=[])
axs[0, 1].plot(centroids.iloc[1], color="orange")
axs[0, 1].set_title("Cluster 1")
axs[0, 1].set(xticklabels=[])
axs[1, 0].plot(centroids.iloc[2], color="green")
axs[1, 0].set_title("Cluster 2")
axs[1, 0].set(xticklabels=[])
axs[1, 1].plot(centroids.iloc[3], color="red")
axs[1, 1].set_title("Cluster 3")
axs[1, 1].set(xticklabels=[])
axs[2, 0].plot(centroids.iloc[4], color="purple")
axs[2, 0].set_title("Cluster 4")
axs[2, 0].set(xticklabels=[])
axs[2, 1].plot(centroids.iloc[5], color="brown")
axs[2, 1].set_title("Cluster 5")
axs[2, 1].set(xticklabels=[])
axs[3, 0].plot(centroids.iloc[6], color="pink")
axs[3, 0].set_title("Cluster 6")
axs[3, 0].set(xticklabels=[])
axs[3, 1].plot(centroids.iloc[7], color="gray")
axs[3, 1].set_title("Cluster 7")
axs[3, 1].set(xticklabels=[])
fig.tight_layout()
plt.show()
for n in range(8):
centroid_name = "Centroid " + str(n)
motif_df = motifsanalysis(
centroids.iloc[n], 15, centroid_name=centroid_name, motif_df=motif_df
)
motif_df = motif_df.rename(
columns={15: "StartPoint", 16: "MinMPDistance", 17: "CentroidName"}
)
motif_df = (
motif_df.sort_values(by="MinMPDistance", ascending=True)
.drop_duplicates(subset="MinMPDistance")
.dropna()
)
motif_df = motif_df.drop(
[
motif_df.index[1],
motif_df.index[3],
motif_df.index[4],
motif_df.index[7],
motif_df.index[8],
motif_df.index[12],
motif_df.index[16],
motif_df.index[17],
]
)
print(motif_df)
my_colors = [
"tab:orange",
"blue",
"tab:orange",
"red",
"purple",
"pink",
"purple",
"pink",
"tab:brown",
"pink",
]
motif_df.index = motif_df["CentroidName"]
motif_df["MinMPDistance"].head(10).plot(
kind="bar", x="CentroidName", y="MinMPDistance", color=my_colors
)
plt.title("Minimum Matrix profile value for each motif couple", fontsize=20)
plt.ylabel("Distance", fontsize=18)
plt.xticks(rotation=20)
plt.show()
motifsanalysis2(centroids.iloc[2], 30, centroid_name="Centroid 2")
motifsanalysis2(centroids.iloc[7], 30, centroid_name="Centroid 7")
# motif_df.head(10).to_csv("musicmotif.csv", index=False)
"""
#scaled dataset
scaler = TimeSeriesScalerMeanVariance()
musi_scaled = pd.DataFrame(scaler.fit_transform(musi.df.values).reshape(musi.df.values.shape[0], musi.df.values.shape[1]))
musi_scaled.index = musi.df.index
print(musi_scaled.info())
print(musi_scaled.head(20))
#build mean time series rock
rock = musi_scaled.loc[musi.feat["genre"] == "Rock"]
rock_mean = rock.mean(axis=0)
print(rock_mean)
rock_mean.plot()
plt.title("Rock Mean")
plt.show()
#noise smooting
w = 50
rock_mean = ((rock_mean - rock_mean.mean())/rock_mean.std()).rolling(window=w).mean()
plt.plot(rock_mean)
plt.title("Rock Mean Noise Smooted")
plt.show()
motifsanalysis(rock_mean, 50)
"""