class Visualization():
"""
Maintains information of a single dataset
and provides methods for its visualization.
"""
def __init__(self, raw_data: pd.DataFrame, n_clusters: int) -> None:
"""Initialize the object.
Arguments:
raw_data -- pandas DataFrame with timestamp as index.
n_clusters -- number of clusters to be used in clustering.
"""
self.graph = StateGraph(n_clusters)
data = self.graph.fit_transform(raw_data)
self.norm_data = pd.DataFrame(
data=self.graph.normalisation.transform(raw_data),
index=raw_data.index,
columns=raw_data.columns)
self.labels = np.array([data["label"].to_numpy()]).T
def get_parallel_plot(self) -> go.Figure:
"""Returns a Figure containing the parallel plot of
clusters' centroids.
"""
fig = px.parallel_coordinates(self.graph.centroids,
color=self.graph.centroids.index,
dimensions=self.graph.centroids.columns)
return fig
def get_PCA(self) -> go.Figure:
"""Returns a Figure containing a PCA plot of clustered data."""
pca = PCA(n_components=2)
pca_result = pca.fit_transform(self.norm_data)
plot_data = pd.DataFrame(np.concatenate((pca_result, self.labels),
axis=1),
columns=["pca_x", "pca_y", "cluster"])
return px.scatter(plot_data, x="pca_x", y="pca_y", color="cluster")
def get_TSNE(self, perplexity: int = 100, n_iter: int = 3000) -> go.Figure:
"""Returns a Figure containing a TSNE plot of clustered data.
Function arguments correspond to standard TSNE arguments.
"""
tsne = TSNE(n_components=2,
verbose=1,
perplexity=perplexity,
n_iter=n_iter)
tsne_result = tsne.fit_transform(self.norm_data)
plot_data = pd.DataFrame(np.concatenate((tsne_result, self.labels),
axis=1),
columns=["tsne_x", "tsne_y", "cluster"])
return px.scatter(plot_data, x="tsne_x", y="tsne_y", color="cluster")
class Visualization():
"""
Maintains information of a single dataset
and provides methods for its visualization.
"""
def __init__(self, raw_data : pd.DataFrame, n_clusters : int) -> None:
"""Initialize the object.
Arguments:
raw_data -- pandas DataFrame with timestamp as index.
n_clusters -- number of clusters to be used in clustering.
"""
self.graph = StateGraph(n_clusters)
self.data = self.graph.fit_transform(raw_data)
self.norm_data = pd.DataFrame(
data=self.graph.normalisation.transform(raw_data),
index=raw_data.index,
columns=raw_data.columns)
self.labels = np.array([self.data["label"].to_numpy()]).T
def get_parallel_plot(self) -> go.Figure:
"""Returns a Figure containing the parallel plot of
clusters' centroids.
"""
fig = px.parallel_coordinates(
self.graph.centroids,
color=self.graph.centroids.index,
dimensions=self.graph.centroids.columns
)
return fig
def get_PCA(self) -> go.Figure:
"""Returns a Figure containing a PCA plot of clustered data."""
pca = PCA(n_components=2)
pca_result = pca.fit_transform(self.norm_data)
plot_data = pd.DataFrame(
np.concatenate((pca_result,self.labels),axis=1),
columns=["pca_x","pca_y","cluster"])
return px.scatter(plot_data,x="pca_x",y="pca_y",color="cluster")
def get_TSNE(self, perplexity : int = 100, n_iter : int = 3000) -> go.Figure:
"""Returns a Figure containing a TSNE plot of clustered data.
Function arguments correspond to standard TSNE arguments.
"""
tsne = TSNE(n_components=2,
verbose=1,
perplexity=perplexity,
n_iter=n_iter)
tsne_result = tsne.fit_transform(self.norm_data)
plot_data = pd.DataFrame(
np.concatenate((tsne_result,self.labels),axis=1),
columns=["tsne_x","tsne_y","cluster"])
return px.scatter(plot_data,x="tsne_x",y="tsne_y",color="cluster")
def get_histograms(self,cluster : int) -> go.Figure:
"""Returns a figure containing a histogram for each sensor
for the data in a given cluster (cluster index starts at 0).
"""
if cluster not in self.labels.flatten():
raise ValueError(f"cluster must be between 0 and {np.amax(self.labels)}")
filtered = self.data[self.data["label"] == cluster]
cols = filtered.columns.values[:-1]
fig = make_subplots(rows=len(cols)//2,cols=2,subplot_titles=cols)
for index, sensor in enumerate(cols):
x = filtered[sensor].values
fig.append_trace(
go.Histogram(x=x, name=sensor),
row=index//2+1,
col=index%2+1
)
fig.update_xaxes(title_text=sensor, row=index//2+1, col=index%2+1)
fig.update_yaxes(title_text="Count", row=index//2+1, col=index%2+1)
fig.update_layout(autosize=False,width=1500,height=200*len(cols),
showlegend=False)
return fig