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Using OOI data in the cloud with PangeoIn this workshop we will use [Pangeo's](http://pangeo.io/) [Binderhub](https://binderhub.readthedocs.io/en/latest/) to do some science with OOI data in the cloud together. Since we are working today on the Binderhub our work will be ephemeral but if you would like to continue wor... | import pandas as pd
import xarray as xr
import hvplot.xarray
import hvplot.pandas
from matplotlib import pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (14, 8) | _____no_output_____ | MIT | ooi-botpt.ipynb | tjcrone/ooi-pangeo-virtual-booth-2021 |
Next find some data on the OOI Data Explorer Link to new OOI Data Explorer: https://dataexplorer.oceanobservatories.org/ | from erddapy import ERDDAP
server = 'http://erddap.dataexplorer.oceanobservatories.org/erddap'
protocol = 'tabledap'
e = ERDDAP(
server=server,
protocol=protocol
)
e.dataset_id = 'ooi-rs03ccal-mj03f-05-botpta301'
e.get_info_url()
info_df = pd.read_csv(e.get_info_url(response='csv'))
info_df.head()
info_df[info... | _____no_output_____ | MIT | ooi-botpt.ipynb | tjcrone/ooi-pangeo-virtual-booth-2021 |
Earthquake catalog from the OOI seismic array at Axial SeamountHere we parse and plot Axial Seamount earthquake catalog data from [William Wilcock's near-real-time automated earthquake location system](http://axial.ocean.washington.edu/). The data we will use is a text file in they HYPO71 output format located here: h... | eqs_url = 'hypo71.dat'
col_names = ['ymd', 'hm', 's', 'lat_deg', 'lat_min', 'lon_deg', 'lon_min',
'depth', 'MW', 'NWR', 'GAP', 'DMIN', 'RMS', 'ERH', 'ERZ', 'ID', 'PMom', 'SMom']
eqs = pd.read_csv(eqs_url, sep = '\s+', header=0, names=col_names)
eqs.head()
from datetime import datetime
def parse_hypo_date(ymd,... | _____no_output_____ | MIT | ooi-botpt.ipynb | tjcrone/ooi-pangeo-virtual-booth-2021 |
Load earthquake catalog pickle file | eqs_df = pd.read_pickle('hypo71.pkl')
eqs_df.head()
eqs_ds = eqs_df.to_xarray()
eqs_ds
eqs_ds = eqs_ds.set_coords(['lat', 'lon'])
eqs_ds
eqs_ds.mw.hvplot.scatter(x='time', datashade=True) | _____no_output_____ | MIT | ooi-botpt.ipynb | tjcrone/ooi-pangeo-virtual-booth-2021 |
https://xarray.pydata.org/en/stable/user-guide/combining.html | all_ds = xr.merge([eqs_ds, botpt_ds])
all_ds
all_ds.mw.plot(marker='.', linestyle='', markersize=1);
all_ds.mw.hvplot.scatter(datashade=True)
all_ds.mw.hvplot.scatter(datashade=True, x='time') | _____no_output_____ | MIT | ooi-botpt.ipynb | tjcrone/ooi-pangeo-virtual-booth-2021 |
Daily Counts | daily_count = all_ds.mw.resample(time='1D').count()
daily_count
daily_count.plot();
fig, ax1 = plt.subplots()
ax1.bar(daily_count.to_series()['2015'].index, daily_count.to_series()['2015'].values, width=3)
ax2 = ax1.twinx()
ax2.plot(botpt_rolling.botpres.to_series()['2015'], color='cyan'); | _____no_output_____ | MIT | ooi-botpt.ipynb | tjcrone/ooi-pangeo-virtual-booth-2021 |
Mapping eq dataLet's make some maps just because we can. | import cartopy.crs as ccrs
import cartopy
import numpy as np
caldera = pd.read_csv('caldera.csv')
caldera.head()
now = pd.Timestamp('now')
eqs_sub = eqs[now-pd.Timedelta(weeks=2):]
ax = plt.axes(projection = ccrs.Robinson(central_longitude=-130))
ax.plot(caldera.lon, caldera.lat, transform=ccrs.Geodetic())
ax.gridlines... | _____no_output_____ | MIT | ooi-botpt.ipynb | tjcrone/ooi-pangeo-virtual-booth-2021 |
OOI Seafloor Camera DataNow let's look at some video data from the [OOI Seafloor Camera](https://oceanobservatories.org/instrument-class/camhd/) system deployed at Axial Volcano on the Juan de Fuca Ridge. We will make use of the [Pycamhd](https://github.com/tjcrone/pycamhd) library, which can be used to extract frames... | dbcamhd_url = 'https://ooiopendata.blob.core.windows.net/camhd/dbcamhd.json'
def show_image(frame_number):
plt.rc('figure', figsize=(12, 6))
plt.rcParams.update({'font.size': 8})
frame = camhd.get_frame(mov.url, frame_number)
fig, ax = plt.subplots();
im1 = ax.imshow(frame);
plt.yticks(np.arange... | _____no_output_____ | MIT | ooi-botpt.ipynb | tjcrone/ooi-pangeo-virtual-booth-2021 |
Data Visualization - Pie Chart: Compare Percentages- Bar Chart: Compare Scores across groups- Histogram: Show frequency of values/value range- Line Chart: Show trend of Scores- Scatter Plot: Show Relationship between a pair of Scores- Map: Show Geo Distribution of data |Type|Variable Y|Variable X||:--:|:--:|:--:||Pie ... | import plotly.plotly as py #Import library and give it an abbreviated name
import plotly.graph_objs as go #go: graph object
from plotly import tools
py.sign_in('USER NAME', 'API TOKEN') #fill in your user name and API token | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
*** Pie Chart | labels = ['Female','Male']
values = [40,20]
trace = go.Pie(labels=labels, values=values)
py.iplot([trace], filename='pie_chart')
#change data labels by re-defining parameter "textinfo"
labels = ['Female','Male']
values = [40,20]
trace = go.Pie(labels=labels, values=values, textinfo='label+value')
py.iplot([trace], ... | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Practice:--- Please download the Hong Kong census data about educational attainment from this link. Create a pie chart to visualize the percentages of different education levels in 2016. The pie chart should meet following requirements: 1. Donut style 2. Change slice colors | #Write down your code here
#---------------------------------------------------------
| _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
*** Bar ChartFor more details: https://plot.ly/python/reference/bar | x = ['Female','Male']
y = [1.6,1.8]
trace = go.Bar(x=x,y=y)
py.iplot([trace], filename='bar_chart')
#Widen the gap between bars by increasing "bargap" parameters in layout
x = ['Female','Male']
y = [40,20]
trace = go.Bar(x=x,y=y)
layout = go.Layout(bargap=0.5)
fig = go.Figure([trace],layout)
py.iplot(fig, filename=... | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Practice:--- Please refer to "Hong Kong Census Educational Attainment.csv". Create a bar chart to visualize the percentages of different education levels in different years, i.e. 2006, 2011 and 2016. The bar chart should meet following requirements: 1. A bar represents a year 2. 100% Stacked bar chart: higher... | #Write down your code here
#---------------------------------------------------------
| _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
*** Break *** HistogramHistogram is a special type of bar chart where one's y value is its count. It is used to show data distribution: viusalize the skewness and central tendency.For more details: https://plot.ly/python/reference/histogram | a=[1,2,3,3,4,4,4,5,5,6,7,3,3,2]
trace=go.Histogram(x=a)
py.iplot([trace],filename='Histogram')
#Change the bins by re-defining "size" parameter in xbins
a=[1,2,3,3,4,4,4,5,5,6,7,3,3,2]
trace=go.Histogram(x=a,xbins={'size':1})
py.iplot([trace],filename='Histogram')
#Convert into a 100% Histogram whose y value is percent... | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Practice:--- Please download YouTube Popularity data from this link. Create three Histograms to visualize the distribution of views, likes, dislikes and comments. The histograms should meet following requirements: 1. One basic histogram to show distribution of "views" 2. One basic histogram to show distribut... | #Write your code here
| _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Line ChartIn Plot.ly, line chart is defined as a special scatter plot whose scatters are connected by lines.For more details: https://plot.ly/python/reference/scatter | #create your first line chart
x=[1,2,3]
y=[10,22,34]
trace1=go.Scatter(x=x,y=y,mode='lines') #mode='lines','markers','lines+markers'
py.iplot([trace1],filename='line chart')
#add markers to it by changing mode to "lines+markers"
x=[1,2,3]
y=[10,22,34]
trace1=go.Scatter(x=x,y=y,mode='lines+markers')
py.iplot([trace1],... | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Practice:--- Please download stock price data from this link. Create a line chart to visualize the trend of these five listed companies. The line chart should meet following requirements: 1. Name lines after companies | #Write your code here
| _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Scatter PlotFor more details: https://plot.ly/python/reference/scatter | #create your first scatter plot
x=[1,2,3,4,5]
y=[10,22,34,40,50]
trace1=go.Scatter(x=x,y=y,mode='markers')
py.iplot([trace1],filename='scatter')
#style the markers
x=[1,2,3,4,5]
y=[10,22,34,40,50]
trace1=go.Scatter(x=x,y=y,mode='markers',marker={'size':10,'color':'red'})
py.iplot([trace1],filename='scatter')
#assign ... | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Practice:--- Please download box office data from this link. Create a 3D scatter plot to visualize these movies. The scatter plot should meet following requirements: 1. X axis represents "Production Budget" 2. Y axis represents "Box Office" 3. Z axis represents "ROI" (Return on Investment) 4. Size scat... | import pandas as pd
movies=pd.read_csv('movies.csv')
colors=[]
for genre in movies['Genre']:
if genre =='Comedy':
colors.extend([1])
else:
colors.extend([len(genre)])
#Write your code here
| _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
*** Break *** MapWe will learn two types of maps: scatter map and filled map. Scatter map is to show scattering points on the geo map while filled map is to show the value of a region by changing its color on the map.For more details: https://plot.ly/python/reference/scattermapbox and https://plot.ly/python/reference... | mapbox_token='YOUR TOKEN' | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Besides, we need to use google map api to search for place's coordinates. So please go to google cloud platform: https://console.cloud.google.com/google/maps-apis and activate Place API. | #install googlemaps library
! pip3 install googlemaps
import googlemaps
place_api='YOUR TOKEN'
client=googlemaps.Client(key=place_api) #create a client variable with your api
univs=client.places('universities in hong kong') #search for some places
type(univs) #look into the search result. It's a dictionary.
univs.keys... | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
2. Filled MapFill regions on the map with certain colors to represent the statistics. This type of map has an academic name of "choropleth map". | import pandas as pd
freedom_table=pd.read_csv('https://juniorworld.github.io/python-workshop-2018/doc/human-freedom-index.csv')
freedom_table.head() #first column, i.e. iso contry code, can be used to create a map.
trace=go.Choropleth(
locations=freedom_table['ISO_code'],
z=freedom_table['human freedom'... | _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Practice:---Please create a world map representing the GDP values of the countries recorded in freedom_table. The map should meet following requirements: 1. colorscale = Reds 2. projection type: natural earth | #Write your code here
| _____no_output_____ | MIT | doc/Class4.ipynb | juniorworld/python-workshop-2018 |
Import Libaries & Define Functions | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import glob
sns.set(style='whitegrid')
def frame_it(path):
csv_files = glob.glob(path + '/*.csv')
df_list = []
for filename in csv_files:
df = pd.read_csv(filename, index_col='Unnamed: 0', header=0)
... | _____no_output_____ | MIT | 1-dl-project/dl-9-baseline-model-analysis.ipynb | luiul/statistics-meets-logistics |
Analysis | # MODIFY!
df = frame_it('./baseline-err')
# we tranpose the data frame for the analysis
df = df.T
# we transpose the data frame due to way we exported the data
df_rmse = df.sort_values('RMSE')
df_rmse
df_rmse.to_csv(f'./analysis/{notebook_name}.csv') | _____no_output_____ | MIT | 1-dl-project/dl-9-baseline-model-analysis.ipynb | luiul/statistics-meets-logistics |
ERR Values [MBit/s] and [(MBit/s)^2] | df_rmse.style.highlight_min(color = 'lightgrey', axis = 0).set_table_styles([{'selector': 'tr:hover','props': [('background-color', '')]}]) | _____no_output_____ | MIT | 1-dl-project/dl-9-baseline-model-analysis.ipynb | luiul/statistics-meets-logistics |
RMSE Performance Decline based on Best Performance [%] | df_rmse_min = df_rmse.apply(lambda value : -((value/df.min())-1),axis=1)
df_rmse_min = df_rmse_min.sort_values('RMSE',ascending=False)
df_rmse_min.to_csv(f'./analysis/{notebook_name}-min.csv')
df_rmse_min.style.highlight_max(color = 'lightgrey', axis = 0).set_table_styles([{'selector': 'tr:hover','props': [('background... | _____no_output_____ | MIT | 1-dl-project/dl-9-baseline-model-analysis.ipynb | luiul/statistics-meets-logistics |
RMSE Performance Increment based on Worst Performance [%] | df_rmse_max = df.apply(lambda value : abs((value/df.max())-1),axis=1)
df_rmse_max = df_rmse_max.sort_values('RMSE',ascending=False)
df_rmse_max.to_csv(f'./analysis/{notebook_name}-max.csv')
df_rmse_max.style.highlight_max(color = 'lightgrey', axis = 0).set_table_styles([{'selector': 'tr:hover','props': [('background-co... | _____no_output_____ | MIT | 1-dl-project/dl-9-baseline-model-analysis.ipynb | luiul/statistics-meets-logistics |
Visualization | ax = sns.barplot(data=df_rmse, x='RMSE',y=df_rmse.index, palette='mako')
show_values_on_bars(ax, "h", 0.1)
ax.set(ylabel='Model',xlabel='RMSE [MBit/s]')
ax.tick_params(axis=u'both', which=u'both',length=0)
ax.set_title('Baseline Model RMSE');
ax = sns.barplot(data=df_rmse_min,x='RMSE',y=df_rmse_min.index,palette='mako... | _____no_output_____ | MIT | 1-dl-project/dl-9-baseline-model-analysis.ipynb | luiul/statistics-meets-logistics |
# Based on Jupyter Notebook created by Josh Tobin for CS 189 at UC Berkeley
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from timeit import default_timer as timer | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow | |
Part 1: Create a synthetic dataset Let's generate some data from a polynomial $$y_i = -0.1 x_i^4 - 0.4 x_i^3 - 0.5 x_i^2 + 0.5 x_i + 1 + \epsilon_i\mbox{, where }\epsilon_i \sim \mathcal{N}(0,0.1), x_i \sim \mathrm{Uniform}(-1,1)$$ | def polynomial(values, coeffs):
assert len(values.shape) == 2
# Coeffs are assumed to be in order 0, 1, ..., n-1
expanded = np.hstack([coeffs[i] * (values ** i) for i in range(0, len(coeffs))])
return np.sum(expanded, axis=-1)
def polynomial_data(coeffs, n_data=100, x_range=[-1, 1], eps=0.1):
x = np... | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
Let's inspect it | # Good to look at shapes, some values
print(x.shape)
print(y.shape)
print(x[:5])
print(y[:5])
def plot_polynomial(coeffs, x_range=[-1, 1], color='red', label='polynomial', alpha=1.0):
values = np.linspace(x_range[0], x_range[1], 1000).reshape([-1, 1])
poly = polynomial(values, coeffs)
plt.plot(values, poly,... | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
Part 2: Ordinary least squares (OLS) Let's code up a naive implementation of the OLS solution $$L(\vec{w}) = \sum_{i=1}^{N} (y_i - \vec{w}^\top\vec{x}_i)^2 = \Vert \vec{y} - X\vec{w} \Vert_2^2$$ $$\tilde{L}(\vec{w}) := {1 \over N}L(\vec{w}) = {1 \over N}\sum_{i=1}^{N} (y_i - \vec{w}^\top\vec{x}_i)^2 \mbox{ ("Mean Squ... | def least_squares(x, y):
xTx = x.T.dot(x)
xTx_inv = np.linalg.inv(xTx)
w = xTx_inv.dot(x.T.dot(y))
return w
def avg_loss(x, y, w):
y_hat = x.dot(w)
loss = np.mean((y - y_hat) ** 2)
return loss | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
How well does it work? $$\hat{y} := wx + b$$ This is equivalent to: $$\hat{y} = \vec{w}^\top\vec{x}\mbox{ where }\vec{w} = \left(\begin{matrix}w \\b \\\end{matrix}\right)\mbox{ and }\vec{x} = \left(\begin{matrix}x \\1 \\\end{matrix}\right)$$ | augmented_x = np.hstack([x, np.ones_like(x)])
linear_coeff = least_squares(augmented_x, y)
loss = avg_loss(augmented_x, y, linear_coeff)
plt.figure(figsize=(10, 5))
plt.scatter(x, y, color='green')
plot_polynomial([linear_coeff[1,0], linear_coeff[0,0]])
print(loss) | 0.03848975511265433
| MIT | OLS.ipynb | Animeshrockn/github-slideshow |
Part 3: Polynomial features $$\vec{x} = \left(\begin{matrix}1 \\x \\x^2 \\\vdots \\x^d \\\end{matrix}\right)$$ $$\hat{y} = \vec{w}^\top\vec{x}\mbox{ where }\vec{w} = \sum_{i=1}^{d}w_i x^i$$ Can fit a model that is *non-linear* in the input with *linear* regression! | def polynomial_features(x, order):
features = np.hstack([x**i for i in range(0, order+1)])
return features
def plot_regression(x, y, degree):
start = timer()
features = polynomial_features(x, degree)
w = least_squares(features, y)
loss = avg_loss(features, y, w)
end = timer()
plt.fi... | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
Part 4: Hyperparameters What degree of polynomial features should we choose? (Previously we picked 4 because we know how the data was generated, but in practice we don't.) This is known as "model selection", since different hyperparameter choices result in different models, so we are effectively choosing the model. | times = []
errs = []
for degree in range(0, 6):
plot_regression(x, y, degree)
def plot_losses(losses, label='loss', color='b'):
plt.plot(losses, color=color, label=label)
plt.semilogy()
plt.legend()
plt.title(f"Minimum loss achieved at hyperparam value {np.argmin(losses)}")
plt.xticks(np.arange(... | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
Loss never goes up as we increase the degree! Should we choose degree 20? | plot_regression(x, y, 20)
plot_regression(x, y, 10)
plot_regression(x, y, 5) | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
Why does this happen? $$\mbox{Recall }\vec{w}^{*} = X^\dagger \vec{y} \mbox{ and } X^\dagger = V \Sigma^{-1} U^\top $$ $$\mbox{Let's take a look at the singular values of }X \mbox{, i.e.: diagonal entries of }\Sigma$$ | features_20 = polynomial_features(x, 20)
features_20.shape
_, singular_values_20, _ = np.linalg.svd(features_20)
singular_values_20.min()
features_5 = polynomial_features(x, 5)
features_5.shape
_, singular_values_5, _ = np.linalg.svd(features_5)
singular_values_5.min() | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
$$\mbox{Very small singular value - } X^\top X \mbox{ is close to being non-invertible. As a result, computing }X^\top X \mbox{ and }X^\dagger\mbox{ is numerically unstable.}$$ | w_20 = least_squares(features_20, y)
np.abs(w_20).max()
w_5 = least_squares(features_5, y)
np.abs(w_5).max() | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
$$\mbox{Since }\vec{w}^{*} := \left( X^\top X \right)^{-1} X^\top \vec{y} = X^\dagger \vec{y}\mbox{, small singular values of }X\mbox{ causes }\vec{w}^{*}\mbox{ to have elements that are large in magnitude.}$$ $$\mbox{This is bad - large coordinate values of }\vec{w}^{*}\mbox{ make the prediction sensitive to tiny chan... | np.random.seed(200)
x_new, y_new = polynomial_data(coeffs, 50)
plt.figure(figsize=(10, 5))
plt.scatter(x, y, color='green')
plt.scatter(x_new, y_new, color='blue')
def plot_regression_new(x, y, x_new, y_new, degree, y_axis_limits = None):
start = timer()
features = polynomial_features(x, degree)
w = least_s... | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
High-degree polynomial doesn't generalize as well to new data. Old data which we have access to is known as "training data" or "train data". New data which we don't have access to is known as "testing data" or "test data". Loss on the old data is known as "training loss" or "train loss", loss on the new data is known a... | plot_regression(x, y, 4)
plot_regression(x_new, y_new, 4)
plot_regression(x, y, 20)
plot_regression(x_new, y_new, 20) | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
This instability is another sign of overfitting. What happens if we had more data? | x_big, y_big = polynomial_data(coeffs, 200)
plot_regression(x_big, y_big, 5)
plot_regression(x_big, y_big, 10)
plot_regression(x_big, y_big, 20) | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
Back to picking the optimal hyperparameters | train_losses = []
test_losses = []
for degree in range(21):
features = polynomial_features(x, degree)
w = least_squares(features, y)
train_loss = avg_loss(features, y, w)
train_losses.append(train_loss)
features_new = polynomial_features(x_new, degree)
test_loss = avg_loss(features_new, y_new, ... | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
The difference between the training loss and the testing loss is known as the "generalization gap". Would like to pick the hyperparameter that results in the lowest testing loss - but can't use testing loss for training or model selection! (Otherwise will overfit to the testing set and make the testing set pointless) ... | N_TRAIN = x.shape[0] // 2
x_train, y_train = x[:N_TRAIN], y[:N_TRAIN]
x_val, y_val = x[N_TRAIN:], y[N_TRAIN:]
train_losses = []
val_losses = []
test_losses = []
for degree in range(21):
features_train = polynomial_features(x_train, degree)
w = least_squares(features_train, y_train)
train_loss = avg_loss(fea... | _____no_output_____ | MIT | OLS.ipynb | Animeshrockn/github-slideshow |
# Getting the dataset into proper place
!mkdir data
!cp '/content/drive/My Drive/datasets/dataset.zip' ./
!unzip -qq dataset.zip -d ./data/
!rm dataset.zip
# Script to generate the processed.csv file
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os
label... | _____no_output_____ | MIT | Crowd_Counter_(InceptionResnetV2).ipynb | imdeepmind/CrowdCounter | |
Excercises Electric Machinery Fundamentals Chapter 3 Problem 3-11 | %pylab notebook | Populating the interactive namespace from numpy and matplotlib
| Unlicense | Chapman/Ch3-Problem_3-11.ipynb | dietmarw/EK5312 |
Description In later years, motors improved and could be run directly from a 60 Hz power supply. As a result, 25 Hz power systems shrank and disappeared. However, there were many perfectly-good working 25 Hz motors in factories around the country that owners were not ready to discard. To keep them running, some users ... | fse1 = 60. # [Hz]
fse2 = 25. # [Hz] | _____no_output_____ | Unlicense | Chapman/Ch3-Problem_3-11.ipynb | dietmarw/EK5312 |
* What combination of poles on the two machines could convert 60 Hz power to 25 Hz power? SOLUTION From Equation, the speed of rotation of the 60 Hz machines would be:$$n_{sm1} = \frac{120f_{se1}}{p_1} = \frac{7200}{p_1}$$and the speed of rotation of the 25 Hz machines would be:$$n_{sm2} = \frac{120f_{se2}}{p_2} = \f... | P1_P2 =(120*fse1) / (120*fse2)
P1_P2 | _____no_output_____ | Unlicense | Chapman/Ch3-Problem_3-11.ipynb | dietmarw/EK5312 |
Let's take an example where $p_1 = 72$. The mechanical speed for machine 1 is therefore: | p1 = 72
n_m1 = (120*fse1)/p1
n_m1 | _____no_output_____ | Unlicense | Chapman/Ch3-Problem_3-11.ipynb | dietmarw/EK5312 |
Calculating the speed for machine 2 gives: | p2 = p1 / P1_P2
n_m2 = (120*fse2)/p2
n_m2 | _____no_output_____ | Unlicense | Chapman/Ch3-Problem_3-11.ipynb | dietmarw/EK5312 |
Convolutional Neural Networks: ApplicationWelcome to Course 4's second assignment! In this notebook, you will:- Implement helper functions that you will use when implementing a TensorFlow model- Implement a fully functioning ConvNet using TensorFlow **After this assignment you will be able to:**- Build and train a Con... | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *
%matplotlib inline
np.random.seed(1) | _____no_output_____ | MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
Run the next cell to load the "SIGNS" dataset you are going to use. | # Loading the data (signs)
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset() | _____no_output_____ | MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
As a reminder, the SIGNS dataset is a collection of 6 signs representing numbers from 0 to 5.The next cell will show you an example of a labelled image in the dataset. Feel free to change the value of `index` below and re-run to see different examples. | # Example of a picture
index = 265
plt.imshow(X_train_orig[index])
print ("y = " + str(np.squeeze(Y_train_orig[:, index]))) | y = 2
| MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
In Course 2, you had built a fully-connected network for this dataset. But since this is an image dataset, it is more natural to apply a ConvNet to it.To get started, let's examine the shapes of your data. | X_train = X_train_orig/255.
X_test = X_test_orig/255.
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
... | number of training examples = 1080
number of test examples = 120
X_train shape: (1080, 64, 64, 3)
Y_train shape: (1080, 6)
X_test shape: (120, 64, 64, 3)
Y_test shape: (120, 6)
| MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
1.1 - Create placeholdersTensorFlow requires that you create placeholders for the input data that will be fed into the model when running the session.**Exercise**: Implement the function below to create placeholders for the input image X and the output Y. You should not define the number of training examples for the m... | # GRADED FUNCTION: create_placeholders
def create_placeholders(n_H0, n_W0, n_C0, n_y):
"""
Creates the placeholders for the tensorflow session.
Arguments:
n_H0 -- scalar, height of an input image
n_W0 -- scalar, width of an input image
n_C0 -- scalar, number of channels of the input
n_... | X = Tensor("X:0", shape=(?, 64, 64, 3), dtype=float32)
Y = Tensor("Y:0", shape=(?, 6), dtype=float32)
| MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
**Expected Output** X = Tensor("Placeholder:0", shape=(?, 64, 64, 3), dtype=float32) Y = Tensor("Placeholder_1:0", shape=(?, 6), dtype=float32) 1.2 - Initialize parametersYou will initialize weights/filters $W1$ and $W2$ using `tf.contrib.layers.xavier_initializer(seed = 0)`. You don't need to worry about bias ... | # GRADED FUNCTION: initialize_parameters
def initialize_parameters():
"""
Initializes weight parameters to build a neural network with tensorflow. The shapes are:
W1 : [4, 4, 3, 8]
W2 : [2, 2, 8, 16]
Returns:
parameters -- a dictionary of tensors containi... | W1 = [ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394
-0.06847463 0.05245192]
W2 = [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058
-0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228
-0.22779644 -0.1601823 -0.16117483 -0.10286498]
| MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
** Expected Output:** W1 = [ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394 -0.06847463 0.05245192] W2 = [-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058 -0.00577033 -0.14643836 0.24162132... | # GRADED FUNCTION: forward_propagation
def forward_propagation(X, parameters):
"""
Implements the forward propagation for the model:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
Arguments:
X -- input dataset placeholder, of shape (input size, number of ex... | Z3 = [[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064]
[-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]]
| MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
**Expected Output**: Z3 = [[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064] [-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]] 1.3 - Compute costImplement the compute cost function below. You might find these two functions helpful: - **tf.nn.sof... | # GRADED FUNCTION: compute_cost
def compute_cost(Z3, Y):
"""
Computes the cost
Arguments:
Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)
Y -- "true" labels vector placeholder, same shape as Z3
Returns:
cost - Tensor of the c... | cost = 2.91034
| MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
**Expected Output**: cost = 2.91034 1.4 Model Finally you will merge the helper functions you implemented above to build a model. You will train it on the SIGNS dataset. You have implemented `random_mini_batches()` in the Optimization programming assignment of course 2. Remember that thi... | # GRADED FUNCTION: model
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009,
num_epochs = 100, minibatch_size = 64, print_cost = True):
"""
Implements a three-layer ConvNet in Tensorflow:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
A... | _____no_output_____ | MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
Run the following cell to train your model for 100 epochs. Check if your cost after epoch 0 and 5 matches our output. If not, stop the cell and go back to your code! | _, _, parameters = model(X_train, Y_train, X_test, Y_test) | Cost after epoch 0: 1.917929
Cost after epoch 5: 1.506757
Cost after epoch 10: 0.955359
Cost after epoch 15: 0.845802
Cost after epoch 20: 0.701174
Cost after epoch 25: 0.571977
Cost after epoch 30: 0.518435
Cost after epoch 35: 0.495806
Cost after epoch 40: 0.429827
Cost after epoch 45: 0.407291
Cost after epoch 50: 0... | MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
**Expected output**: although it may not match perfectly, your expected output should be close to ours and your cost value should decrease. **Cost after epoch 0 =** 1.917929 **Cost after epoch 5 =** 1.506757 **Train Accuracy =** 0.940741 ... | fname = "images/thumbs_up.jpg"
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(64,64))
plt.imshow(my_image) | _____no_output_____ | MIT | Deep Learning Specialisation/Convolutional Neural Networks/Convolution model - Application.ipynb | rakeshbal99/Machine-Learning-Coursera |
View source on GitHub Notebook Viewer Run in binder Run in Google Colab ConvolutionsTo perform linear convolutions on images, use `image.convolve()`. The only argument to convolve is an `ee.Kernel` which is specified by a shape and the weights in the kernel. Each pixel of the image output by `convolve()... | import subprocess
try:
import geehydro
except ImportError:
print('geehydro package not installed. Installing ...')
subprocess.check_call(["python", '-m', 'pip', 'install', 'geehydro'])
# Import libraries
import ee
import folium
import geehydro
# Authenticate and initialize Earth Engine API
try:
e... | _____no_output_____ | MIT | Image/06_convolutions.ipynb | giswqs/earthengine-py-documentation |
Create an interactive map This step creates an interactive map using [folium](https://github.com/python-visualization/folium). The default basemap is the OpenStreetMap. Additional basemaps can be added using the `Map.setOptions()` function. The optional basemaps can be `ROADMAP`, `SATELLITE`, `HYBRID`, `TERRAIN`, or `... | Map = folium.Map(location=[40, -100], zoom_start=4)
Map.setOptions('HYBRID') | _____no_output_____ | MIT | Image/06_convolutions.ipynb | giswqs/earthengine-py-documentation |
Add Earth Engine Python script | # Load and display an image.
image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_044034_20140318')
Map.setCenter(-121.9785, 37.8694, 11)
Map.addLayer(image, {'bands': ['B5', 'B4', 'B3'], 'max': 0.5}, 'input image')
# Define a boxcar or low-pass kernel.
# boxcar = ee.Kernel.square({
# 'radius': 7, 'units': 'pixels', 'norm... | _____no_output_____ | MIT | Image/06_convolutions.ipynb | giswqs/earthengine-py-documentation |
The output of convolution with the low-pass filter should look something like Figure 1. Observe that the arguments to the kernel determine its size and coefficients. Specifically, with the `units` parameter set to pixels, the `radius` parameter specifies the number of pixels from the center that the kernel will cover. ... | Map = folium.Map(location=[40, -100], zoom_start=4)
Map.setOptions('HYBRID')
# Define a Laplacian, or edge-detection kernel.
laplacian = ee.Kernel.laplacian8(1, False)
# Apply the edge-detection kernel.
edgy = image.convolve(laplacian)
Map.addLayer(edgy,
{'bands': ['B5', 'B4', 'B3'], 'max': 0.5},
... | _____no_output_____ | MIT | Image/06_convolutions.ipynb | giswqs/earthengine-py-documentation |
Note the format specifier in the visualization parameters. Earth Engine sends display tiles to the Code Editor in JPEG format for efficiency, however edge tiles are sent in PNG format to handle transparency of pixels outside the image boundary. When a visual discontinuity results, setting the format to PNG results in a... | # Create a list of weights for a 9x9 kernel.
list = [1, 1, 1, 1, 1, 1, 1, 1, 1]
# The center of the kernel is zero.
centerList = [1, 1, 1, 1, 0, 1, 1, 1, 1]
# Assemble a list of lists: the 9x9 kernel weights as a 2-D matrix.
lists = [list, list, list, list, centerList, list, list, list, list]
# Create the kernel from t... | {'type': 'Kernel.fixed', 'width': 9, 'height': 9, 'weights': '\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0]\n [1.0, 1.0... | MIT | Image/06_convolutions.ipynb | giswqs/earthengine-py-documentation |
A Decision Tree of Observable Operators Part 1: NEW Observables.> source: http://reactivex.io/documentation/operators.htmltree. > (transcribed to RxPY 1.5.7, Py2.7 / 2016-12, Gunther Klessinger, [axiros](http://www.axiros.com)) **This tree can help you find the ReactiveX Observable operator you’re looking for.** Ta... | reset_start_time(O.just)
stream = O.just({'answer': rand()})
disposable = subs(stream)
sleep(0.5)
disposable = subs(stream) # same answer
# all stream ops work, its a real stream:
disposable = subs(stream.map(lambda x: x.get('answer', 0) * 2)) |
========== return_value ==========
module rx.linq.observable.returnvalue
@extensionclassmethod(Observable, alias="just")
def return_value(cls, value, scheduler=None):
Returns an observable sequence that contains a single element,
using the specified scheduler to send out observer messages.
There is an al... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
..that was returned from a function *called at subscribe-time*: **[start](http://reactivex.io/documentation/operators/start.html)** | print('There is a little API difference to RxJS, see Remarks:\n')
rst(O.start)
def f():
log('function called')
return rand()
stream = O.start(func=f)
d = subs(stream)
d = subs(stream)
header("Exceptions are handled correctly (an observable should never except):")
def breaking_f():
return 1 / 0
stre... | There is a little API difference to RxJS, see Remarks:
========== start ==========
module rx.linq.observable.start
@extensionclassmethod(Observable)
def start(cls, func, scheduler=None):
Invokes the specified function asynchronously on the specified
scheduler, surfacing the result through an observable sequ... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
..that was returned from an Action, Callable, Runnable, or something of that sort, called at subscribe-time: **[from](http://reactivex.io/documentation/operators/from.html)** | rst(O.from_iterable)
def f():
log('function called')
return rand()
# aliases: O.from_, O.from_list
# 1.: From a tuple:
stream = O.from_iterable((1,2,rand()))
d = subs(stream)
# d = subs(stream) # same result
# 2. from a generator
gen = (rand() for j in range(3))
stream = O.from_iterable(gen)
d = subs(stream)
... |
========== from_callback ==========
module rx.linq.observable.fromcallback
@extensionclassmethod(Observable)
def from_callback(cls, func, mapper=None):
Converts a callback function to an observable sequence.
Keyword arguments:
func -- {Function} Function with a callback as the last parameter to
... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...after a specified delay: **[timer](http://reactivex.io/documentation/operators/timer.html)** | rst()
# start a stream of 0, 1, 2, .. after 200 ms, with a delay of 100 ms:
stream = O.timer(200, 100).time_interval()\
.map(lambda x: 'val:%s dt:%s' % (x.value, x.interval))\
.take(3)
d = subs(stream, name='observer1')
# intermix directly with another one
d = subs(stream, name='observer2') |
0.8 M New subscription on stream 274470005
3.4 M New subscription on stream 274470005
| MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...that emits a sequence of items repeatedly: **[repeat](http://reactivex.io/documentation/operators/repeat.html) ** | rst(O.repeat)
# repeat is over *values*, not function calls. Use generate or create for function calls!
subs(O.repeat({'rand': time.time()}, 3))
header('do while:')
l = []
def condition(x):
l.append(1)
return True if len(l) < 2 else False
stream = O.just(42).do_while(condition)
d = subs(stream)
|
========== repeat ==========
module rx.linq.observable.repeat
@extensionclassmethod(Observable)
def repeat(cls, value=None, repeat_count=None, scheduler=None):
Generates an observable sequence that repeats the given element the
specified number of times, using the specified scheduler to send out
observer... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...from scratch, with custom logic and cleanup (calling a function again and again): **[create](http://reactivex.io/documentation/operators/create.html) ** | rx = O.create
rst(rx)
def f(obs):
# this function is called for every observer
obs.on_next(rand())
obs.on_next(rand())
obs.on_completed()
def cleanup():
log('cleaning up...')
return cleanup
stream = O.create(f).delay(200) # the delay causes the cleanup called before the subs gets the va... |
========== generate_with_relative_time ==========
module rx.linq.observable.generatewithrelativetime
@extensionclassmethod(Observable)
def generate_with_relative_time(cls, initial_state, condition, iterate,
Generates an observable sequence by iterating a state from an
initial state until the condition fails.... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...for each observer that subscribes OR according to a condition at subscription time: **[defer / if_then](http://reactivex.io/documentation/operators/defer.html) ** | rst(O.defer)
# plural! (unique per subscription)
streams = O.defer(lambda: O.just(rand()))
d = subs(streams)
d = subs(streams) # gets other values - created by subscription!
# evaluating a condition at subscription time in order to decide which of two streams to take.
rst(O.if_then)
cond = True
def should_run():
re... |
========== if_then ==========
module rx.linq.observable.ifthen
@extensionclassmethod(Observable)
def if_then(cls, condition, then_source, else_source=None, scheduler=None):
Determines whether an observable collection contains values.
Example:
1 - res = reactivex.Observable.if(condition, obs1)
2 - re... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...that emits a sequence of integers: **[range](http://reactivex.io/documentation/operators/range.html) ** | rst(O.range)
d = subs(O.range(0, 3)) |
========== range ==========
module rx.linq.observable.range
@extensionclassmethod(Observable)
def range(cls, start, count, scheduler=None):
Generates an observable sequence of integral numbers within a
specified range, using the specified scheduler to send out observer
messages.
1 - res = reactivex.... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...at particular intervals of time: **[interval](http://reactivex.io/documentation/operators/interval.html) **(you can `.publish()` it to get an easy "hot" observable) | rst(O.interval)
d = subs(O.interval(100).time_interval()\
.map(lambda x, v: '%(interval)s %(value)s' \
% ItemGetter(x)).take(3)) |
========== interval ==========
module rx.linq.observable.interval
@extensionclassmethod(Observable)
def interval(cls, period, scheduler=None):
Returns an observable sequence that produces a value after each
period.
Example:
1 - res = reactivex.Observable.interval(1000)
2 - res = reactivex.Observ... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...after a specified delay (see timer) ...that completes without emitting items: **[empty](http://reactivex.io/documentation/operators/empty-never-throw.html) ** | rst(O.empty)
d = subs(O.empty()) |
========== empty ==========
module rx.linq.observable.empty
@extensionclassmethod(Observable)
def empty(cls, scheduler=None):
Returns an empty observable sequence, using the specified scheduler
to send out the single OnCompleted message.
1 - res = reactivex.empty()
2 - res = reactivex.empty(rx.Sched... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...that does nothing at all: **[never](http://reactivex.io/documentation/operators/empty-never-throw.html) ** | rst(O.never)
d = subs(O.never()) |
========== never ==========
0.7 T18 [next] 104.4: 0 -1.0 (observer hash: 274473797)
1.1 T18 [next] 104.8: 1 -0.5 (observer hash: 274473797)module rx.linq.observable.never
@extensionclassmethod(Observable)
def never(cls):
Returns a non-terminating observable sequence, which can be used to
denote a... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
...that excepts: **[throw](http://reactivex.io/documentation/operators/empty-never-throw.html) ** | rst(O.on_error)
d = subs(O.on_error(ZeroDivisionError)) |
========== throw ==========
module rx.linq.observable.throw
@extensionclassmethod(Observable, alias="throw_exception")
def on_error(cls, exception, scheduler=None):
Returns an observable sequence that terminates with an exception,
using the specified scheduler to send out the single OnError message.
1 -... | MIT | notebooks/reactivex.io/A Decision Tree of Observable Operators. Part I - Creation.ipynb | christiansandberg/RxPY |
`networkx` supports a lot of graph types: simple graph, simple digraph (parallel edges are not acceptable), multidigraph, multigraphs. Read more [here](https://networkx.github.io/documentation/stable/reference/classes/index.html). For sure, we want to use multidigraph to handle our maps | G = nx.MultiDiGraph()
# add arbitrary nodes
nodes = [i for i in range(1,20)]
G.add_nodes_from(nodes)
num_edges = 70
for _ in range(num_edges):
u = random.randint(1, 101)
v = random.randint(1,101)
G.add_edge(u, v, weight = 5)
nx.draw(G) | _____no_output_____ | Apache-2.0 | networkx.ipynb | sierraone/GettingStarted |
One thing that you have probably noticed that the graph that was returned by `osmnx` in the first tutorial could be dealt with as if it was a pure vanilla `networkx` graph. That is a very good news for us, because we can use [`networkx` utilities and algorithms](https://networkx.github.io/documentation/stable/reference... | nx.number_strongly_connected_components(G)
# we will take the first 10 simple cycles
for cycle in itertools.islice(nx.simple_cycles(G), 10):
print(cycle)
# it returns an iterators so we unpack it
print(*nx.all_pairs_shortest_path(G)) | (1, {1: [1], 16: [1, 16], 70: [1, 70], 79: [1, 79], 69: [1, 69], 53: [1, 53], 48: [1, 16, 48], 56: [1, 16, 56], 9: [1, 16, 9], 81: [1, 16, 81], 4: [1, 16, 4], 6: [1, 16, 6], 37: [1, 70, 37], 84: [1, 70, 84], 64: [1, 70, 64], 52: [1, 70, 52], 3: [1, 79, 3], 8: [1, 79, 8], 22: [1, 79, 22], 93: [1, 69, 93], 46: [1, 69, 46... | Apache-2.0 | networkx.ipynb | sierraone/GettingStarted |
Okay let's load UofT map again and see what can we do | G = ox.graph_from_address("university of toronto", dist = 300)
fig, ax = ox.plot_graph(G)
# here are the nodes
[*G.nodes()]
# this will take a while
# this can come in handy in a lot of case studies
for distance_to_others in itertools.islice(nx.all_pairs_shortest_path(G), 5): # we will take only five
print(distance... | (130170945, {130170945: [130170945], 55808527: [130170945, 55808527], 389677905: [130170945, 389677905], 127284680: [130170945, 127284680], 127284677: [130170945, 127284677], 55808564: [130170945, 55808527, 55808564], 55808512: [130170945, 55808527, 55808512], 2143434279: [130170945, 55808527, 2143434279], 3996671926: ... | Apache-2.0 | networkx.ipynb | sierraone/GettingStarted |
English | response = requests.get(videos_url_en)
page = BeautifulSoup(response.text, 'html5lib')
content_divs = page.find_all('div', class_='content-inner')
len(content_divs)
for content_div in content_divs:
video_block = content_div.find('div', class_='video-block')
video_wrapper = video_block.find('div', class_='sqs-v... | _____no_output_____ | MIT | notebooks/3. Web Scraping Starter.ipynb | NathanMaton/forked_sushi_chef |
Burmese | response = requests.get(videos_url_my)
page2 = BeautifulSoup(response.text, 'html5lib')
content_divs2 = page2.find_all('div', class_='content-inner')
len(content_divs2)
for content_div in content_divs2:
video_block = content_div.find('div', class_='video-block')
video_wrapper = video_block.find('div', class_='... | https://player.vimeo.com/video/262570817?app_id=122963&wmode=opaque
https://player.vimeo.com/video/262755072?app_id=122963&wmode=opaque
https://player.vimeo.com/video/262755467?app_id=122963&wmode=opaque
https://player.vimeo.com/video/262755673?app_id=122963&wmode=opaque
https://player.vimeo.com/video/267661918?app_id=... | MIT | notebooks/3. Web Scraping Starter.ipynb | NathanMaton/forked_sushi_chef |
**This is an example Notebook for running training on Higgs vs background signal classification. ** **Background:** High-energy collisions at the Large Hadron Collider (LHC) produce particles that interact with particle detectors. One important task is to classify different types of collisions based on their physics co... | !wget https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz | --2022-02-25 23:13:36-- https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz
Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252
Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 281... | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
2. Unzip the dataset folder | !gzip -d HIGGS.csv.gz
from sklearn.datasets import make_gaussian_quantiles
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import p... | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Load the file using pandas library** | data=pd.read_csv('./HIGGS.csv')
data | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
Assign first column 0 to class labels (labeled 1 for signal, 0 for background) and all others to feature matrix X.In this example, for the sake of fast checking, we use 1000 samples. To train on the entire dataset, proceed with uncommenting the lines below. | X=data.iloc[:1000,1:]#data.iloc[:,1:]
y=data.iloc[:1000,0]#data.iloc[:,0] | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
Split your data into training and validation samples where the fraction of the data used for validation is 33%. | X_train1, X_val, y_train1, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X_train1, y_train1, test_size=0.2, random_state=42) | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Visualize your data - One histogram per feature column** Detailed information on what each feature column is can be found in *Attribute Information* section on the [UCI Machine learning Repositery](https://archive.ics.uci.edu/ml/datasets/HIGGS). For further information, refer to the [paper](https://www.nature.com/art... | from itertools import combinations
import matplotlib.pyplot as plt
fig, axes = plt.subplots(len(X_train.columns)//3, 3, figsize=(12, 48))
i = 0
for triaxis in axes:
for axis in triaxis:
X_train.hist(column = X_train.columns[i], bins = 100, ax=axis)
i = i+1 | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Setup the Boosted Decision Tree model** (BDT explanation [here](https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/boosted-decision-tree-regression:~:text=Boosting%20means%20that%20each%20tree,small%20risk%20of%20less%20coverage.)) | classifier = AdaBoostClassifier(
DecisionTreeClassifier(max_depth=1),
n_estimators=200
) | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Train the Boosted Decision Tree model** | from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_breast_cancer
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import Label... | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Predict on new testing data** | predictions = classifier.predict(X_test) | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Print confusion matrix which describes the performance of the model classification by displaying the number of True Positives, True Negatives, False Positives and False Negatives. More info on [Wikipedia](https://en.wikipedia.org/wiki/Confusion_matrix)** | confusion_matrix(y_test, predictions) | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Setup the Neural Network** (some useful info [here](https://towardsdatascience.com/a-gentle-introduction-to-neural-networks-series-part-1-2b90b87795bc)) | from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense
model_nn = Sequential()
model_nn.add(Dense(28, input_dim=28, activation='relu'))
model_nn.add(Dense(8, activation='relu'))
model_nn.add(Dense(1, activation='sigmoid')) | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Train the Neural Network and save your model weights in a h5 file** | # compile the keras model
model_nn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit the keras model on the dataset
history=model_nn.fit(X, y,validation_data=(X_val,y_val),epochs=5, batch_size=10)
# evaluate the keras model
_, accuracy = model_nn.evaluate(X, y)
model_nn.save('my_model.h5... | dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
| Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Plot accuracy wrt number of epochs** |
# summarize history for accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
| _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Plot training loss wrt number of epochs** | # summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
y_pred=model_nn.predict(X_test)
confusion_matrix(y_test, y_pred.round()) | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
**Plot the ROC (Receiver Operating Characteristic) Curve** (more info on ROC could be found [here](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) | !pip install plot-metric
from plot_metric.functions import BinaryClassification
# Visualisation with plot_metric
bc = BinaryClassification(y_pred.round(), y_test, labels=["Class 1", "Class 2"])
# Figures
plt.figure(figsize=(5,5))
bc.plot_roc_curve()
plt.show() | _____no_output_____ | Apache-2.0 | Higgs_Classification/higgs_classification.ipynb | MonitSharma/MS_Thesis |
Combining DataPractice combining data from two different data sets. In the same folder as this Jupyter notebook, there are two csv files:* rural_population_percent.csv* electricity_access_percent.csvThey both come from the World Bank Indicators data. * https://data.worldbank.org/indicator/SP.RUR.TOTL.ZS* https://data.... | pd.read_json('http://api.worldbank.org/v2/indicator/SP.RUR.TOTL.ZS/?format=json')
# TODO: import the pandas library
import pandas as pd
# TODO: read in each csv file into a separate variable
# HINT: remember from the Extract material that these csv file have some formatting issues
# HINT: The file paths are 'rural_pop... | _____no_output_____ | MIT | lessons/ETLPipelines/5_combinedata_exercise/.ipynb_checkpoints/5_combining_data-checkpoint.ipynb | GooseHuang/Udacity-Data-Scientist-Nanodegree |
Exercise 2 (Challenge)This exercise is more challenging.The resulting data frame should look like this:|Country Name|Country Code|Year|Rural_Value|Electricity_Value||--|--|--|--|--|--||Aruba|ABW|1960|49.224|49.239|... etc.Order the results in the dataframe by country and then by yearHere are a few pandas methods that ... | # TODO: merge the data sets together according to the instructions. First, use the
# melt method to change the formatting of each data frame so that it looks like this:
# Country Name, Country Code, Year, Rural Value
# Country Name, Country Code, Year, Electricity Value
# TODO: drop any columns from the data frames t... | _____no_output_____ | MIT | lessons/ETLPipelines/5_combinedata_exercise/.ipynb_checkpoints/5_combining_data-checkpoint.ipynb | GooseHuang/Udacity-Data-Scientist-Nanodegree |
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