I am following this example on sklearn documentation
I want to change the limits of y axis so I can visually compare results from different models.
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_diabetes
from sklearn.tree import DecisionTreeRegressor
from sklearn.inspection import PartialDependenceDisplay
diabetes = load_diabetes()
X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
y = diabetes.target
tree = DecisionTreeRegressor()
tree.fit(X, y)
fig, ax = plt.subplots(figsize=(12, 6))
ax.set_ylim(50,300)
tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age", "bmi"], ax=ax)
However, it seems that ax.set_ylim get ignored no matter what I specify. On the other hand, ax.set_title given in example works fine.
PartialDependenceDisplay have an axes_ attribute that represents both matplotlib's axes of the figure.
You can modify them as follow:
tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age", "bmi"], ax=ax)
tree_disp.axes_[0][0].set_ylim(50,300)
tree_disp.axes_[0][1].set_ylim(50,300)
This will output the following plot:
Related
is there a way to get TwoSlopeNorm in combination with base 2 ticks on the colorbar?
An example is something like this where you have normal linear scaling:
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
x = np.arange(-50,100,1)
y = x.copy()
c = x.copy()
scatter_plot = plt.scatter(x, y, c=c, cmap='bwr', norm=matplotlib.colors.TwoSlopeNorm(vmin=-50, vcenter=0, vmax=100))
cbar = plt.colorbar(scatter_plot)
plt.show()
I know based on a previous question of mine that SymLogNorm supports base2, but it looks like this is not the case for TwoSlopeNorm. Does anyone have a suggestion on how to do it?
So this is my code, it's written a little messy and my result is absolutely ridiculous. I have no idea how to fix it.
Also, the seaborn library does not work on my computer in any way.
.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data=pd.read_csv('Data.csv',encoding="latin1",sep=";",engine="python")
table = data.replace(0, 0.1)
plt.plot(table["RMDM"], table["BSURF"], color="#03012d", marker=".", ls="None", markersize=3, label="")
data['RMDM'] = data['RMDM'].astype(float)
data['BSURF'] = data['BSURF'].astype(float)
fig, ax = plt.subplots()
x=data['BSURF']
y=data['RMDM']
ax.set_yscale('log')
ax.set_xscale('log')
plt.style.use('classic')
plt.xlabel('B_LC')
plt.ylabel('RM/DM')
plt.plot(x,y, 'og')
from scipy.stats import linregress
df = data.loc[(data['RMDM'] >0) & (data['BSURF'] >0)]
stats = linregress(np.log10(df["RMDM"]),np.log10(df["BSURF"]))
m = stats.slope
b = stats.intercept
r = stats.rvalue
x = np.logspace(-1, 5, base=10)
y = (m*x+b)
plt.plot(x, y, c='orange', label="fit")
plt.legend()
#m,c=np.polyfit(x,y,1)
#plt.plot(x,m*x+c)
plt.grid()
plt.show()
lmplot can be used to create a linear line through your data. you correctly used np.log for the linear regression data. keep x in terms of the log.
df['log_col1']=np.log(df['col1'])
sns.lmplot(x='log_col1','y='target', data=df, ci=None)
sns.scatterplot(y='target',x='log_col1',data=df)
plt.show()
I have a pandas series containing numbers ranging between 0 and 100. I want to visualise it in a horizontal bar consisting of 3 main colours.
I have tried using seaborn but all I can get is a heatmap matrix. I have also tried the below code, which is producing what I need but not in the way I need it.
x = my_column.values
y = x
t = x
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(x, y, c=t, cmap='brg')
ax2.scatter(x, y, c=t, cmap='brg')
plt.show()
What I'm looking for is something similar to the below figure, how can I achieve that using matplotlib or seaborn?
The purpose of this is not quite clear, however, the following would produce an image like the one shown in the question:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
x = np.linspace(100,0,101)
fig, ax = plt.subplots(figsize=(6,1), constrained_layout=True)
cmap = LinearSegmentedColormap.from_list("", ["limegreen", "gold", "crimson"])
ax.imshow([x], cmap=cmap, aspect="auto",
extent=[x[0]-np.diff(x)[0]/2, x[-1]+np.diff(x)[0]/2,0,1])
ax.tick_params(axis="y", left=False, labelleft=False)
plt.show()
I am trying to generate an interactive plot that depends on widgets.
The problem I have is that when I change parameters using the slider, a new plot is done after the previous one, instead I would expect only one plot changing according to the parameters.
Example:
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
def plot_func(freq):
x = np.linspace(0, 2*np.pi)
y = np.sin(x * freq)
plt.plot(x, y)
interact(plot_func, freq = widgets.FloatSlider(value=7.5,
min=1,
max=5.0,
step=0.5))
After moving the slider to 4.0, I have:
while I just want one figure to change as I move the slider.
How can I achieve this?
(I am using Python 2.7, matplotlib 2.0 and I have just updated notebook and jupyter to the latest version. let me know if further info is needed.)
As you want to change the figure, instead of creating a new one, may I suggest the following way:
Use an interactive backend; %matplotlib notebook
Update the line in the plot, instead of drawing new ones.
So the code could look something like this:
%matplotlib notebook
from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2 * np.pi)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
line, = ax.plot(x, np.sin(x))
def update(w = 1.0):
line.set_ydata(np.sin(w * x))
fig.canvas.draw_idle()
interact(update);
Alternatively you may use plt.show() as in this answer.
This is an issue (?) introduced in the last version of jupyter and/or ipywidgets. One workaround I found was to add the line plt.show() at the end of plot_func.
For completion, here is an answer that makes use of more than one slider bar and sets the default parameters as well as the interval lengths.
%matplotlib notebook
from ipywidgets import *
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-10, 10,100)
def f(x, A, B, C):
return A*x**2 + B*x + C
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
line, = ax.plot(x, f(x, A=1, B=1, C=1))
def update(A = 1, B = 0, C = 0):
line.set_ydata(f(x,A,B,C))
fig.canvas.draw_idle()
interact(update, A = (-4,4,0.1), B = (-4,4,0.1), C = (-4,4,0.1));
I'm trying to plot over a wide range with a log-scaled axis, but I want to show 10^{-1}, 10^0, 10^1 as just 0.1, 1, 10. ScalarFormatter will change everything to integers instead of scientific notation, but I'd like most of the tickmark labels to be scientific; I'm only wanting to change a few of the labels. So the MWE is
import numpy as np
import matplotlib as plt
fig = plt.figure(figsize=[7,7])
ax1 = fig.add_subplot(111)
ax1.set_yscale('log')
ax1.set_xscale('log')
ax1.plot(np.logspace(-4,4), np.logspace(-4,4))
plt.show()
and I want the middle labels on each axis to read 0.1, 1, 10 instead of 10^{-1}, 10^0, 10^1
Thanks for any help!
When setting set_xscale('log'), you're using a LogFormatterSciNotation (not a ScalarFormatter). You may subclass LogFormatterSciNotation to return the desired values 0.1,1,10 if they happen to be marked as ticks.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import LogFormatterSciNotation
class CustomTicker(LogFormatterSciNotation):
def __call__(self, x, pos=None):
if x not in [0.1,1,10]:
return LogFormatterSciNotation.__call__(self,x, pos=None)
else:
return "{x:g}".format(x=x)
fig = plt.figure(figsize=[7,7])
ax = fig.add_subplot(111)
ax.set_yscale('log')
ax.set_xscale('log')
ax.plot(np.logspace(-4,4), np.logspace(-4,4))
ax.xaxis.set_major_formatter(CustomTicker())
plt.show()
Update: With matplotlib 2.1 there is now a new option
Specify minimum value to format as scalar for LogFormatterMathtext
LogFormatterMathtext now includes the option to specify a minimum value exponent to format as a scalar (i.e., 0.001 instead of 10-3).
This can be done as follows, by using the rcParams (plt.rcParams['axes.formatter.min_exponent'] = 2):
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['axes.formatter.min_exponent'] = 2
fig = plt.figure(figsize=[7,7])
ax = fig.add_subplot(111)
ax.set_yscale('log')
ax.set_xscale('log')
ax.plot(np.logspace(-4,4), np.logspace(-4,4))
plt.show()
This results in the same plot as above.
Note however that this limit is symmetric, it would not allow to set only 1 and 10, but not 0.1. Hence the initial solution is more generic.