I am trying to produce a visualization of the SOM mapping for the Iris dataset ( https://archive.ics.uci.edu/ml/datasets/Iris).
My code so far:
from sklearn.datasets import load_iris
from mvpa2.suite import *
import pandas as pd
import numpy as np
df = pd.read_csv(filepath_or_buffer='data/iris.data', header=None, sep=',')
df.columns=['sepal_len', 'sepal_wid', 'petal_len', 'petal_wid', 'class']
df.dropna(how="all", inplace=True) # drops the empty line at file-end
# split the data table into feature data x and class labels y
x = df.ix[:,0:4].values # the first 4 columns are the features
y = df.ix[:,4].values # the last column is the class label
t = np.zeros(len(y), dtype=int)
t[y == 'Iris-setosa'] = 0
t[y == 'Iris-versicolor'] = 1
t[y == 'Iris-virginica'] = 2
som = SimpleSOMMapper((240, 320), 100, learning_rate=0.05)
som.train(x)
pl.imshow(som.K, origin='lower')
mapped = som(x)
for i, m in enumerate(mapped):
pl.text(m[1], m[0], t[i], ha='center', va='center',
bbox=dict(facecolor='white', alpha=0.5, lw=0))
pl.show()
which produces this mapping:
Is there any way to customize the palette so it looks nicer like this one? (taken from https://github.com/JustGlowing/minisom)?
Basically I am trying to use a nicer palette (perhaps with fewer colors) and mark the class labels in a nicer way.
Thank you.
I will answer my own question: it turns out that I forgot to slice my data:
pl.imshow(som.K[:,:,0], origin='lower')
Everything looks fine now:
Related
Am trying to find hist()'s figsize and layout parameter for sns.pairplot().
I have a pairplot that gives me nice scatterplots between the X's and y. However, it is oriented horizontally and there is no equivalent layout parameter to make them vertical to my knowledge. 4 plots per row would be great.
This is my current sns.pairplot():
sns.pairplot(X_train,
x_vars = X_train.select_dtypes(exclude=['object']).columns,
y_vars = ["SalePrice"])
This is what I would like it to look like: Source
num_mask = train_df.dtypes != object
num_cols = train_df.loc[:, num_mask[num_mask == True].keys()]
num_cols.hist(figsize = (30,15), layout = (4,10))
plt.show()
What you want to achieve isn't currently supported by sns.pairplot, but you can use one of the other figure-level functions (sns.displot, sns.catplot, ...). sns.lmplot creates a grid of scatter plots. For this to work, the dataframe needs to be in "long form".
Here is a simple example. sns.lmplot has parameters to leave out the regression line (fit_reg=False), to set the height of the individual subplots (height=...), to set its aspect ratio (aspect=..., where the subplot width will be height times aspect ratio), and many more. If all y ranges are similar, you can use the default sharey=True.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# create some test data with different y-ranges
np.random.seed(20230209)
X_train = pd.DataFrame({"".join(np.random.choice([*'uvwxyz'], np.random.randint(3, 8))):
np.random.randn(100).cumsum() + np.random.randint(100, 1000) for _ in range(10)})
X_train['SalePrice'] = np.random.randint(10000, 100000, 100)
# convert the dataframe to long form
# 'SalePrice' will get excluded automatically via `melt`
compare_columns = X_train.select_dtypes(exclude=['object']).columns
long_df = X_train.melt(id_vars='SalePrice', value_vars=compare_columns)
# create a grid of scatter plots
g = sns.lmplot(data=long_df, x='SalePrice', y='value', col='variable', col_wrap=4, sharey=False)
g.set(ylabel='')
plt.show()
Here is another example, with histograms of the mpg dataset:
import matplotlib.pyplot as plt
import seaborn as sns
mpg = sns.load_dataset('mpg')
compare_columns = mpg.select_dtypes(exclude=['object']).columns
mpg_long = mpg.melt(value_vars=compare_columns)
g = sns.displot(data=mpg_long, kde=True, x='value', common_bins=False, col='variable', col_wrap=4, color='crimson',
facet_kws={'sharex': False, 'sharey': False})
g.set(xlabel='')
plt.show()
For a ML project I'm currently on, I need to verify if the trained data are good or not.
Let's say that I'm "splitting" the sky into several altitude grids (let's take 3 values for the moment) and for a given region (let's say, Europe).
One grid could be a signal reception strength (RSSI), another one the signal quality (RSRQ)
Each cell of the grid is therefor a rectangle and it has a mean value of each measurement (i.e. RSSI or RSRQ) performed in that area.
I have hundreds of millions of data
In the code below, I know how to draw a coloured mesh with xarray for each altitude: I just use xr.plot.pcolormesh(lat,lon, the_data_set); that's fine
But this will only give me a "flat" figure like this:
RSSI value at 3 different altitudes
I need to draw all the pcolormesh() of a dataset for each altitude in such way that:
1: I can have the map at the bottom
2: Each pcolormesh() is stacked and "displayed" at its altitude
3: I need to add a 3d scatter plot for testing my trained data
4: Need to be interactive as I have to zoom in areas
For 2 and 3 above, I managed to do something using plt and cartopy :
enter image description here
But plt/cartopy combination is not as interactive as plotly.
But plotly doesn't have the pcolormesh functionality
And still ... I don't know in anycase, how to "stack" the pcolormesh results that I did get above.
I've been digging Internet for few days but I didn't find something that could satisfy all my criteria.
What I did to get my pcolormesh:
import numpy as np
import xarray as xr
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
class super_data():
def __init__(self, lon_bound,lat_bound,alt_bound,x_points,y_points,z_points):
self.lon_bound = lon_bound
self.lat_bound = lat_bound
self.alt_bound = alt_bound
self.x_points = x_points
self.y_points = y_points
self.z_points = z_points
self.lon, self.lat, self.alt = np.meshgrid(np.linspace(self.lon_bound[0], self.lon_bound[1], self.x_points),
np.linspace(self.lat_bound[0], self.lat_bound[1], self.y_points),
np.linspace(self.alt_bound[0], self.alt_bound[1], self.z_points))
self.this_xr = xr.Dataset(
coords={'lat': (('latitude', 'longitude','altitude'), self.lat),
'lon': (('latitude', 'longitude','altitude'), self.lon),
'alt': (('latitude', 'longitude','altitude'), self.alt)})
def add_data_array(self,ds_name,ds_min,ds_max):
def create_temp_data(ds_min,ds_max):
data = np.random.randint(ds_min,ds_max,size=self.y_points * self.x_points)
return data
temp_data = []
# Create "z_points" number of layers in the z axis
for i in range(self.z_points):
temp_data.append(create_temp_data(ds_min,ds_max))
data = np.concatenate(temp_data)
data = data.reshape(self.z_points,self.x_points, self.y_points)
self.this_xr[ds_name] = (("altitude","longitude","latitude"),data)
def plot(self,dataset, extent=None, plot_center=False):
# I want t
if np.sqrt(self.z_points) == np.floor(np.sqrt(self.z_points)):
side_size = int(np.sqrt(self.z_points))
else:
side_size = int(np.floor(np.sqrt(self.z_points) + 1))
fig = plt.figure()
i_ax=1
for i in range(side_size):
for j in range(side_size):
if i_ax < self.z_points+1:
this_dataset = self.this_xr[dataset].sel(altitude=i_ax-1)
# Initialize figure with subplots
ax = fig.add_subplot(side_size, side_size, i_ax, projection=ccrs.PlateCarree())
i_ax += 1
ax.coastlines()
this_dataset.plot.pcolormesh('lon', 'lat', ax=ax, infer_intervals=True, alpha=0.5)
else:
break
plt.tight_layout()
plt.show()
if __name__ == "__main__":
# Wanted coverage :
lons = [-15, 30]
lats = [35, 65]
alts = [1000, 5000]
xarr = super_data(lons,lats,alts,10,8,3)
# Add some fake data
xarr.add_data_array("RSSI",-120,-60)
xarr.add_data_array("pressure",700,1013)
xarr.plot("RSSI",0)
Thanks for you help
How can a distribution histogram similar to this one be constructed based on the data from the table?
enter image description here
enter image description here
Code python:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_excel('Data.xlsx')
print(df)
df.plot.hist(df)
plt.show()
It isn't clear exactly what the x and y axes of your desired plot are. Hopefully this will get you started. Sometimes trying to comeup with a MRE will help you solve your own problem.
import random
import pandas as pd
import matplotlib.pyplot as plt
#######################################
# generate some random data for a MWE #
#######################################
random.seed(22)
data = [random.randint(0, 100) for _ in range(0, 10)]
data = pd.Series(sorted(data))
freqs = [random.uniform(0, 1) for _ in range(0, 10)]
freqs = sorted(freqs)
freqs = pd.Series(freqs)
df = pd.DataFrame()
df['data'] = data
df['frequencies'] = freqs
###############################################
# Desired bar plot using pandas built in plot #
###############################################
df.plot(x='data', y='frequencies', kind='bar')
plt.show()
I have made a violin plot with the code below (see pic for plot). I'm wondering if it's possible to get p-values of the differences between samples on the x axis. This could be any statistical test that shows a p-value, so if there is a global shift in the violin plot, the difference could be seen.
violin plot
Edit:
For clarity, was hoping to add something like this to show pvals between samples:
for i,p in enumerate(pvals):
if p>=0.05:
displaystring = r'n.s.'
elif p<0.0001:
displaystring = r'***'
elif p<0.001:
displaystring = r'**'
else:
displaystring = r'*'
Python code for making violin plot:
#!/usr/bin/env python
"""
Usage: Run script in ~/snakemake_eclip/scripts, use help function to see which parameters are needed.
This script takes in the all_reads_matrix made by merge_matrix.py and creates a violin plot.
"""
import pandas as pd
import argparse
import matplotlib.pyplot as plt
import os
import seaborn as sns
import numpy as np
plt.switch_backend('agg')
from scipy import stats
import numpy as np
def make_violin(in_matrix, save_path):
df = pd.read_csv(str(in_matrix), index_col=False)
# remove outliers
Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
IQR = Q3 - Q1
df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]
# drop zeros
df = df[(df != 0).all(1)]
df = df.iloc[:, 1:].transform(lambda x: np.log(x / x.sum()))
print(df)
plt.figure(figsize=(20, 10), dpi=300)
sns.violinplot(data=df)
plt.plot()
plt.title("Read Counts of Individual ENSG")
plt.xlabel("Samples")
plt.ylabel("Log Transformed Normalized Read Count")
plt.savefig(os.path.join(str(save_path), 'all_reads_matrix_violin_plot_norm_log.pdf'))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Create a violin plot from all_reads_matrix.csv')
parser.add_argument("--in_matrix",
help='name of input matrix')
parser.add_argument("--save_path",
help='path to save')
# parse out arguments
args = parser.parse_args()
# mutate matrix columns
make_violin(args.in_matrix, args.save_path)
I have data that represent a time series of categorical variables. I want to display the transitions in categories below a traditional line plot of related continuous time series to show off context as time evolves. I'd like to know the best way to do this. My attempt was in terms of Rectangles. The appearance is a bit weird, and importantly the axis labels for the x axis don't render as dates.
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from pandas.plotting import register_matplotlib_converters
import matplotlib.dates as mdates
register_matplotlib_converters()
t0 = pd.DatetimeIndex(["2017-06-01 00:00","2017-06-17 00:00","2017-07-03 00:00","2017-08-02 00:00","2017-08-09 00:00","2017-09-01 00:00"])
t1 = pd.DatetimeIndex(["2017-06-01 00:00","2017-08-15 00:00","2017-09-01 00:00"])
df0 = pd.DataFrame({"cat":[0,2,1,2,0,1]},index = t0)
df1 = pd.DataFrame({"op":[0,1,0]},index=t1)
# Create new plot
fig,ax = plt.subplots(1,figsize=(8,3))
data_layout = {
"cat" : {0: ('bisque','Low'),
1: ('lightseagreen','Medium'),
2: ('rebeccapurple','High')},
"op" : {0: ('darkturquoise','Open'),
1: ('tomato','Close')}
}
vars =("cat","op")
dfs = [df0,df1]
all_ticks = []
leg = []
for j,(v,d) in enumerate(zip(vars,dfs)):
dvals = d[v][:].astype("d")
normal = mpl.colors.Normalize(vmin=0, vmax=2.)
colors = plt.cm.Set1(0.75*normal(dvals.as_matrix()))
handles = []
for i in range(d.count()-1):
s = d[v].index.to_pydatetime()
level = d[v][i]
base = d[v].index[i]
w = s[i+1] - s[i]
patch=mpl.patches.Rectangle((base,float(j)),width=w,color=data_layout[v][level][0],height=1,fill=True)
ax.add_patch(patch)
for lev in data_layout[v]:
print data_layout[v][level]
handles.append(mpl.patches.Patch(color=data_layout[v][lev][0],label=data_layout[v][lev][1]))
all_ticks.append(j+0.5)
leg.append( plt.legend(handles=handles,loc = (3-3*j+1)))
plt.axhline(y=1.,linewidth=3,color="gray")
plt.xlim(pd.Timestamp(2017,6,1).to_pydatetime(),pd.Timestamp(2017,9,1).to_pydatetime())
plt.ylim(0,2)
ax.add_artist(leg[0]) # two legends on one axis
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d') # This fails
plt.yticks(all_ticks,vars)
plt.show()
which produces this with no dates and has jittery lines:. How do I fix this? Is there a better way entirely?
This is a way to display dates on x-axis:
In your code substitute the line that fails with this one:
ax.xaxis.set_major_formatter((mdates.DateFormatter('%Y-%m-%d')))
But I don't remember how it should look like, can you show us the end-result again?