Update and rename CASE4.py to whisky_classification.py

Author: prateekiiest

CASE4.py

@@ -0,0 +1,184 @@
+# First, we import a tool to allow text to pop up on a plot when the cursor
+# hovers over it.  Also, we import a data structure used to store arguments
+# of what to plot in Bokeh.  Finally, we will use numpy for this section as well!
+
+from bokeh.models import HoverTool, ColumnDataSource
+import numpy as np
+
+# Let's plot a simple 5x5 grid of squares, alternating in color as red and blue.
+
+plot_values = [1,2,3,4,5]
+plot_colors = ["red", "blue"]
+
+# How do we tell Bokeh to plot each point in a grid?  Let's use a function that
+# finds each combination of values from 1-5.
+from itertools import product
+
+grid = list(product(plot_values, plot_values))
+print(grid)
+
+# The first value is the x coordinate, and the second value is the y coordinate.
+# Let's store these in separate lists.
+
+xs, ys = zip(*grid)
+print(xs)
+print(ys)
+
+# Now we will make a list of colors, alternating between red and blue.
+
+colors = [plot_colors[i%2] for i in range(len(grid))]
+print(colors)
+
+# Finally, let's determine the strength of transparency (alpha) for each point,
+# where 0 is completely transparent.
+
+alphas = np.linspace(0, 1, len(grid))
+
+# Bokeh likes each of these to be stored in a special dataframe, called
+# ColumnDataSource.  Let's store our coordinates, colors, and alpha values.
+
+source = ColumnDataSource(
+    data={
+        "x": xs,
+        "y": ys,
+        "colors": colors,
+        "alphas": alphas,
+    }
+)
+# We are ready to make our interactive Bokeh plot!
+
+output_file("Basic_Example.html", title="Basic Example")
+fig = figure(tools="resize, hover, save")
+fig.rect("x", "y", 0.9, 0.9, source=source, color="colors",alpha="alphas")
+hover = fig.select(dict(type=HoverTool))
+hover.tooltips = {
+    "Value": "@x, @y",
+    }
+show(fig)
+
+
+
+cluster_colors = ["red", "orange", "green", "blue", "purple", "gray"]
+regions = ["Speyside", "Highlands", "Lowlands", "Islands", "Campbelltown", "Islay"]
+
+region_colors = {regions[i] : cluster_colors[i] for i in range(len(cluster_colors))}     
+        
+print(region_colors)
+
+
+distilleries = list(whisky.Distillery)
+correlation_colors = []
+for i in range(len(distilleries)):
+    for j in range(len(distilleries)):
+        if (correlations[i][j] < 0.7):                     # if low correlation,
+            correlation_colors.append('white')         # just use white.
+        else:                                          # otherwise,
+            if (whisky.Group[i]==whisky.Group[j]):              # if the groups match,
+                correlation_colors.append(cluster_colors[whisky.Group[i]]) # color them by their mutual group.
+            else:                                      # otherwise
+                correlation_colors.append('lightgray') # color them lightgray.
+                
+                
+                
+                
+                
+source = ColumnDataSource(
+    data = {
+        "x": np.repeat(distilleries,len(distilleries)),
+        "y": list(distilleries)*len(distilleries),
+        "colors": correlation_colors,
+        "alphas": correlations.flatten(),
+        "correlations": correlations.flatten()
+    }
+)
+
+output_file("Whisky Correlations.html", title="Whisky Correlations")
+fig = figure(title="Whisky Correlations",
+    x_axis_location="above", tools="resize,hover,save",
+    x_range=list(reversed(distilleries)), y_range=distilleries)
+fig.grid.grid_line_color = None
+fig.axis.axis_line_color = None
+fig.axis.major_tick_line_color = None
+fig.axis.major_label_text_font_size = "5pt"
+fig.xaxis.major_label_orientation = np.pi / 3
+
+fig.rect('x', 'y', .9, .9, source=source,
+     color='colors', alpha='alphas')
+hover = fig.select(dict(type=HoverTool))
+hover.tooltips = {
+    "Whiskies": "@x, @y",
+    "Correlation": "@correlations",
+}
+show(fig)
+
+
+
+points = [(0,0), (1,2), (3,1)]
+xs, ys = zip(*points)
+colors = ["red", "blue", "green"]
+
+output_file("Spatial_Example.html", title="Regional Example")
+location_source = ColumnDataSource(
+    data={
+        "x": xs,
+        "y": ys,
+        "colors": colors,
+    }
+)
+
+fig = figure(title = "Title",
+    x_axis_location = "above", tools="resize, hover, save")
+fig.plot_width  = 300
+fig.plot_height = 380
+fig.circle("x", "y", 10, 10, size=10, source=location_source,
+     color='colors', line_color = None)
+
+hover = fig.select(dict(type = HoverTool))
+hover.tooltips = {
+    "Location": "(@x, @y)"
+}
+show(fig)
+
+
+
+
+
+
+def location_plot(title, colors):
+    output_file(title+".html")
+    location_source = ColumnDataSource(
+        data={
+            "x": whisky[" Latitude"],
+            "y": whisky[" Longitude"],
+            "colors": colors,
+            "regions": whisky.Region,
+            "distilleries": whisky.Distillery
+        }
+    )
+    
+    fig = figure(title = title,
+        x_axis_location = "above", tools="resize, hover, save")
+    fig.plot_width  = 400
+    fig.plot_height = 500
+    fig.circle("x", "y", 10, 10, size=9, source=location_source,
+         color='colors', line_color = None)
+    fig.xaxis.major_label_orientation = np.pi / 3
+    hover = fig.select(dict(type = HoverTool))
+    hover.tooltips = {
+        "Distillery": "@distilleries",
+        "Location": "(@x, @y)"
+    }
+    show(fig)
+
+region_cols = [region_colors[whisky.Region[i]] for i in range(len(whisky.Region))]
+location_plot("Whisky Locations and Regions", region_cols)
+
+
+
+region_cols = [region_colors[whisky.Region[i]] for i in range(len(whisky.Region))]
+classification_cols = [cluster_colors[whisky.Group[i]] for i in range(len(whisky.Group))]
+
+location_plot("Whisky Locations and Regions", region_cols)
+location_plot("Whisky Locations and Groups", classification_cols)
+
+