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MotifVSShaplet.py
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MotifVSShaplet.py
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import utils
clusters = pd.read_csv("musicluster_dtw_index.csv", index_col="Unnamed: 0")
cluster0 = clusters[clusters["ClusterLabel"] == 0]
cluster1 = clusters[clusters["ClusterLabel"] == 1]
cluster2 = clusters[clusters["ClusterLabel"] == 2]
cluster3 = clusters[clusters["ClusterLabel"] == 3]
cluster4 = clusters[clusters["ClusterLabel"] == 4]
cluster5 = clusters[clusters["ClusterLabel"] == 5]
cluster6 = clusters[clusters["ClusterLabel"] == 6]
cluster7 = clusters[clusters["ClusterLabel"] == 7]
# let's plot our motif
motifs = pd.read_csv("musicmotif.csv")
print(motifs)
motifs = motifs.drop(columns=["StartPoint", "MinMPDistance", "CentroidName"])
sns.set()
fig, axs = plt.subplots(5, 2, figsize=(10, 12))
axs[0, 0].plot(motifs.iloc[0], color="orange")
axs[0, 0].set_title("Cluster 1")
axs[0, 0].set(xticklabels=[])
axs[0, 1].plot(motifs.iloc[1], color="blue")
axs[0, 1].set_title("Cluster 0")
axs[0, 1].set(xticklabels=[])
axs[1, 0].plot(motifs.iloc[2], color="orange")
axs[1, 0].set_title("Cluster 1")
axs[1, 0].set(xticklabels=[])
axs[1, 1].plot(motifs.iloc[3], color="red")
axs[1, 1].set_title("Cluster 3")
axs[1, 1].set(xticklabels=[])
axs[2, 0].plot(motifs.iloc[4], color="purple")
axs[2, 0].set_title("Cluster 4")
axs[2, 0].set(xticklabels=[])
axs[2, 1].plot(motifs.iloc[5], color="pink")
axs[2, 1].set_title("Cluster 6")
axs[2, 1].set(xticklabels=[])
axs[3, 0].plot(motifs.iloc[6], color="purple")
axs[3, 0].set_title("Cluster 4")
axs[3, 0].set(xticklabels=[])
axs[3, 1].plot(motifs.iloc[7], color="pink")
axs[3, 1].set_title("Cluster 6")
axs[3, 1].set(xticklabels=[])
axs[4, 0].plot(motifs.iloc[8], color="brown")
axs[4, 0].set_title("Cluster 5")
axs[4, 0].set(xticklabels=[])
axs[4, 1].plot(motifs.iloc[9], color="pink")
axs[4, 1].set_title("Cluster 6")
axs[4, 1].set(xticklabels=[])
fig.tight_layout()
plt.show()
"""
#distance dtw between motifs
for i in range(10):
for y in range(10):
if i != y:
dist = dtw(motifs.iloc[i], motifs.iloc[y])
if dist < 4:
print("Distance between ", i, "and ", y)
print(dist)
print("Finished")
"""
"""
Distance between 4 and 7
3.000682959869468
Distance between 0 and 8
3.1748638275716208
Distance between 0 and 5
3.4931194805559893
"""
sns.set(
rc={"figure.figsize": (10, 4)},
)
# plot 4/7
plt.title("DTW: 3.001")
plt.subplot(1, 2, 1) # row 1, col 2 index 1
plt.plot(motifs.iloc[4], color="purple")
plt.title("Motif from centroid 4 ")
plt.xticks([])
plt.subplot(1, 2, 2) # index 2
plt.plot(motifs.iloc[7], color="pink")
plt.title("Motif from centroid 6")
plt.xticks([])
plt.show()
# plot 0/8
plt.title("DTW: 3.175")
plt.subplot(1, 2, 1) # row 1, col 2 index 1
plt.plot(motifs.iloc[0], color="orange")
plt.title("Motif from centroid 1 ")
plt.xticks([])
plt.subplot(1, 2, 2) # index 2
plt.plot(motifs.iloc[8], color="brown")
plt.title("Motif from centroid 5")
plt.xticks([])
plt.show()
# plot 0/5
plt.title("DTW: 3.493")
plt.subplot(1, 2, 1) # row 1, col 2 index 1
plt.plot(motifs.iloc[0], color="orange")
plt.title("Motif from centroid 1 ")
plt.xticks([])
plt.subplot(1, 2, 2) # index 2
plt.plot(motifs.iloc[5], color="pink")
plt.title("Motif from centroid 6")
plt.xticks([])
plt.show()
# let's find somethig interesting between:
# 4 and 6, 1 and 5, 1 and 6
# remember that cluster 5 has the higher MinMPDistance
tracks = utils.load_tracks("data/tracks.csv", outliers=False, fill=False)
print(tracks.info())
tracks0 = tracks[tracks.index.isin(cluster0.index)]
tracks1 = tracks[tracks.index.isin(cluster1.index)]
tracks2 = tracks[tracks.index.isin(cluster2.index)]
tracks3 = tracks[tracks.index.isin(cluster3.index)]
tracks4 = tracks[tracks.index.isin(cluster4.index)]
tracks5 = tracks[tracks.index.isin(cluster5.index)]
tracks6 = tracks[tracks.index.isin(cluster6.index)]
tracks7 = tracks[tracks.index.isin(cluster7.index)]
print("All dataset")
print(tracks["track", "favorites"].describe())
plt.show()
statistics = pd.DataFrame()
row0 = []
row0 = np.append(row0, round(tracks0["track", "comments"].mean(), 2))
row0 = np.append(row0, round(tracks0["track", "date_created"].mean(), 2))
row0 = np.append(row0, round(tracks0["track", "favorites"].mean(), 2))
row0 = np.append(row0, round(tracks0["track", "interest"].mean(), 2))
row0 = np.append(row0, round(tracks0["track", "listens"].mean(), 2))
statistics = statistics.append(pd.Series(row0), ignore_index=True)
row1 = []
row1 = np.append(row1, round(tracks1["track", "comments"].mean(), 2))
row1 = np.append(row1, round(tracks1["track", "date_created"].mean(), 2))
row1 = np.append(row1, round(tracks1["track", "favorites"].mean(), 2))
row1 = np.append(row1, round(tracks1["track", "interest"].mean(), 2))
row1 = np.append(row1, round(tracks1["track", "listens"].mean(), 2))
statistics = statistics.append(pd.Series(row1), ignore_index=True)
row2 = []
row2 = np.append(row2, round(tracks2["track", "comments"].mean(), 2))
row2 = np.append(row2, round(tracks2["track", "date_created"].mean(), 2))
row2 = np.append(row2, round(tracks2["track", "favorites"].mean(), 2))
row2 = np.append(row2, round(tracks2["track", "interest"].mean(), 2))
row2 = np.append(row2, round(tracks2["track", "listens"].mean(), 2))
statistics = statistics.append(pd.Series(row2), ignore_index=True)
row3 = []
row3 = np.append(row3, round(tracks3["track", "comments"].mean(), 2))
row3 = np.append(row3, round(tracks3["track", "date_created"].mean(), 2))
row3 = np.append(row3, round(tracks3["track", "favorites"].mean(), 2))
row3 = np.append(row3, round(tracks3["track", "interest"].mean(), 2))
row3 = np.append(row3, round(tracks3["track", "listens"].mean(), 2))
statistics = statistics.append(pd.Series(row3), ignore_index=True)
row4 = []
row4 = np.append(row4, round(tracks4["track", "comments"].mean(), 2))
row4 = np.append(row4, round(tracks4["track", "date_created"].mean(), 2))
row4 = np.append(row4, round(tracks4["track", "favorites"].mean(), 2))
row4 = np.append(row4, round(tracks4["track", "interest"].mean(), 2))
row4 = np.append(row4, round(tracks4["track", "listens"].mean(), 2))
statistics = statistics.append(pd.Series(row4), ignore_index=True)
row5 = []
row5 = np.append(row5, round(tracks5["track", "comments"].mean(), 2))
row5 = np.append(row5, round(tracks5["track", "date_created"].mean(), 2))
row5 = np.append(row5, round(tracks5["track", "favorites"].mean(), 2))
row5 = np.append(row5, round(tracks5["track", "interest"].mean(), 2))
row5 = np.append(row5, round(tracks5["track", "listens"].mean(), 2))
statistics = statistics.append(pd.Series(row5), ignore_index=True)
row6 = []
row6 = np.append(row6, round(tracks6["track", "comments"].mean(), 2))
row6 = np.append(row6, round(tracks6["track", "date_created"].mean(), 2))
row6 = np.append(row6, round(tracks6["track", "favorites"].mean(), 2))
row6 = np.append(row6, round(tracks6["track", "interest"].mean(), 2))
row6 = np.append(row6, round(tracks6["track", "listens"].mean(), 2))
statistics = statistics.append(pd.Series(row6), ignore_index=True)
row7 = []
row7 = np.append(row7, round(tracks7["track", "comments"].mean(), 2))
row7 = np.append(row7, round(tracks7["track", "date_created"].mean(), 2))
row7 = np.append(row7, round(tracks7["track", "favorites"].mean(), 2))
row7 = np.append(row7, round(tracks7["track", "interest"].mean(), 2))
row7 = np.append(row7, round(tracks7["track", "listens"].mean(), 2))
statistics = statistics.append(pd.Series(row7), ignore_index=True)
statistics = statistics.rename(
columns={
0: "comments",
1: "date_created",
2: "favorites",
3: "interest",
4: "listens",
}
)
print(statistics)
statistics["comments"].plot(kind="bar")
plt.title("Comments")
plt.show()
statistics["date_created"].plot(kind="bar")
plt.title("Date Created")
plt.show()
statistics["favorites"].plot(kind="bar")
plt.title("Favorites")
plt.show()
statistics["interest"].plot(kind="bar")
plt.title("Interest")
plt.show()
statistics["listens"].plot(kind="bar")
plt.title("Listens")
plt.show()