I have a dataset where each sample/row is a unique protein and that protein is quantified across 7 features/columns. This dataset includes thousands of proteins and will be classified by machine learning (Support Vector Machine). To give an example of the data:
Protein
Feature 1
Feature 2
Feature 3
Feature 4
Feature 5
Feature 6
Feature 7
Protein 1
10.0
8.7
5.4
28.0
7.9
11.3
5.3
Protein 2
6.5
9.3
4.8
2.7
12.3
14.2
0.7
...
...
...
...
...
...
...
...
Protein N
8.0
6.8
4.9
6.2
10.0
19.3
4.8
In addition to this dataset, I also have 2 more replicates that are structured the exact same and have the same proteins for a total of 3 replicates. Normally if I wanted to visualize one of these datasets, I could transform my 7 features using PCA and plot the first two principal components with each point/protein colored by its classification. However, is there a way that I can take my 3 replicates and get some sort of "consensus" PCA plot for them?
I've seen two possible solutions for handling this:
Average each feature for each protein to get a single dataset with N rows and 7 columns, then PCA transform and plot
Concatenate the 3 replicates into a single dataset such that each row now has 7x3 columns, then PCA transform and plot
To clarify what's being said in solution 2, let's call Feature 1 from replicate 1 Feature 1.1, Feature 1 from replicate 2 Feature 1.2, etc.:
Protein
Feature 1.1
...
Feature 7.1
Feature 1.2
...
Feature 7.2
Feature 1.3
...
Feature 7.3
Protein 1
10.0
...
5.3
8.4
...
5.9
9.7
...
5.2
Protein 2
6.5
...
0.7
6.8
...
0.8
6.3
...
0.7
...
...
...
...
...
...
...
...
...
...
Protein N
8.0
...
4.8
7.9
...
4.9
8.1
...
4.7
What I'm looking for is if there's an accepted solution for such a problem or if there's a solution that's more statistically sound. Thanks in advance!
Related
my data is X(x1,x2,x3) and Y(y1). Y contain label classes.for example:
datase=[1.2 4.5 10.32 1; 1.7 5.7 10.12 1; 0.9 6.1 9.99 0;...;1.9 7.8 6.67 0]
I want to classify my data via chain markov,But all Python code is available for textual data and other applications. Does anyone know a code sample for classifying numerical data?
Background:
I'm currently developing some data profiling in SQL Server. This consists of calculating aggregate statistics on the values in targeted columns.
I'm using SQL for most of the heavy lifting, but calling Python for some of the statistics that SQL is poor at calculating. I'm leveraging the Pandas package through SQL Server Machine Language Services.
However,
I'm currently developing this script on Visual Studio. The SQL portion is irrelevant other than as background.
Problem:
My issue is that when I call one of the Python statistics functions, it produces the output as a series with the labels seemingly not part of the data. I cannot access the labels at all. I need the values of these labels, and I need to normalize the data and insert a column with static values describing which calculation was performed on that row.
Constraints:
I will need to normalize each statistic so I can union the datasets and pass the values back to SQL for further processing. All output needs to accept dynamic schemas, so no hardcoding labels etc.
Attempted solutions:
I've tried explicitly coercing output to dataframes. This just results in a series with label "0".
I've also tried adding static values to the columns. This just adds the target column name as one of the inaccessible labels, and the intended static value as part of the series.
I've searched many times for a solution, and couldn't find anything relevant to the problem.
Code and results below. Using the iris dataset as an example.
###########################
## AGG STATS TEST SCRIPT
##
###########################
#LOAD MODULES
import pandas as pds
#GET SAMPLE DATASET
iris = pds.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
#CENTRAL TENDENCY
mode1 = iris.mode()
stat_mode = pds.melt(
mode1
)
stat_median = iris.median()
stat_median['STAT_NAME'] = 'STAT_MEDIAN' #Try to add a column with the value 'STAT_MEDIAN'
#AGGREGATE STATS
stat_describe = iris.describe()
#PRINT RESULTS
print(iris)
print(stat_median)
print(stat_describe)
###########################
## OUTPUT
##
###########################
>>> #PRINT RESULTS
... print(iris) #ORIGINAL DATASET
...
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
.. ... ... ... ... ...
145 6.7 3.0 5.2 2.3 virginica
146 6.3 2.5 5.0 1.9 virginica
147 6.5 3.0 5.2 2.0 virginica
148 6.2 3.4 5.4 2.3 virginica
149 5.9 3.0 5.1 1.8 virginica
[150 rows x 5 columns]
>>> print(stat_median) #YOU CAN SEE THAT IT INSERTED COLUMN INTO ROW LABELS, VALUE INTO RESULTS SERIES
sepal_length 5.8
sepal_width 3
petal_length 4.35
petal_width 1.3
STAT_NAME STAT_MEDIAN
dtype: object
>>> print(stat_describe) #BASIC DESCRIPTIVE STATS, NEED TO LABEL THE STATISTIC NAMES TO UNPIVOT THIS
sepal_length sepal_width petal_length petal_width
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.057333 3.758000 1.199333
std 0.828066 0.435866 1.765298 0.762238
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000
>>>
Any assistance is greatly appreciated. Thank you!
I figured it out. There's a function called reset_index that will convert the index to a column, and create a new numerical index.
stat_median = pds.DataFrame(stat_median)
stat_median.reset_index(inplace=True)
stat_median = stat_median.rename(columns={'index' : 'fieldname', 0: 'value'})
stat_median['stat_name'] = 'median'
I do have a large dataset (around 8 million rows x 25 columns) in Pandas and I am struggling to find a way to compute weighted average on this dataframe which in turn creates another data frame.
Here is how my dataset looks like (very simplified version of it):
prec temp
location_id hours
135 1 12.0 4.0
2 14.0 4.1
3 14.3 3.5
4 15.0 4.5
5 15.0 4.2
6 15.0 4.7
7 15.5 5.1
136 1 12.0 4.0
2 14.0 4.1
3 14.3 3.5
4 15.0 4.5
5 15.0 4.2
6 15.0 4.7
7 15.5 5.1
I have a multi-index on [location_id, hours]. I have around 60k locations and 140 hours for each location (making up the 8 million rows).
The rest of the data is numeric (float) or categorical. I have only included 2 columns here, normally there are around 20 columns.
What I am willing to do is to create a new data frame that is basically a weighted average of this data frame. The requirements indicate that 12 of these location_ids should be averaged out by a specified weight to form the combined_location_id values.
For example, location_ids 1,3,5,7,9,11,13,15,17,19,21,23 with their appropriate weights (separate data coming in from another data frame) should be weighted averaged to from the combined_location_id CL_1's data.
That is a lot of data to handle and I wasn't able to find a completely Pandas way of solving it. Therefore, I went with a for loop approach. It is extremely slow and I am sure this is not the right way to do it:
def __weighted(self, ds, weights):
return np.average(ds, weights=weights)
f = {'hours': 'first', 'location_id': 'first',
'temp': lambda x: self.__weighted(x, weights), 'prec': lambda x: self.__weighted(x, weights)}
data_frames = []
for combined_location in all_combined_locations:
mapped_location_ids = combined_location.location_ids
weights = combined_location.weights_of_location_ids
data_for_this_combined_location = pd.concat(df_data.loc[df_data.index.get_level_values(0) == location_id] for location_id in mapped_location_ids)
data_grouped_by_distance = data_for_this_combined_location.groupby("hours", as_index=False)
data_grouped_by_distance = data_grouped_by_distance.agg(f)
data_frames.append(data_grouped_by_distance)
df_combined_location_data = pd.concat(data_frames)
df_combined_location_data.set_index(['location_id', 'hours'], inplace=True)
This works well functionally, however the performance and the memory consumption is horrible. It is taking over 2 hours on my dataset and that is currently not acceptable. The existence of the for loop is an indicator that this could be handled better.
Is there a better/faster way to implement this?
From what I saw you can reduce one for loop with mapped_location_ids
data_for_this_combined_location = df_data.loc[df_data.index.get_level_values(0).isin(mapped_location_ids)]
I'd like to find a way to determine which neighbors are actually used in my knn algorithm, so I can dive deeper into the rows of data that are similar to my features.
Here is an example of a dataset which I split into a training set and a test set for the prediction model:
Player PER VORP WS
Fabricio Oberto 11.9 1.0 4.1
Eddie Johnson 16.5 1.7 4.8
Tim Legler 15.9 2.0 6.8
Ersan Ilyasova 14.3 0.7 3.8
Kevin Love 25.4 3.5 10.0
Tim Hardaway 20.6 5.1 11.7
Frank Brickowsk 8.6 -0.2 1.6
etc....
And here is an example of my knn algorithm code:
features = ['PER','VORP']
knn = KNeighborsRegressor(n_neighbors=5, algorithm='brute')
knn.fit(train[features], train['WS'])
predictions = knn.predict(test[features])
Now, I'm aware that the algorithm will iterate over each row and make each target prediction based on the 5 closest neighbors that come from the target features I've specified.
I'd like to find out WHICH 5 n_neighbors were actually used in determining my target feature? In this case - which players were actually used in determining the target?
Is there a way to get a list of the 5 neighbors (aka players) which were used in the analysis for each row?
knn.kneighbors will return you an array of the corresponding nearest neighbours.
My question is about preprocessing csv files before inputing them into a neural network.
I want to build a deep neural network for the famous iris dataset using tflearn in python 3.
Dataset: http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
I'm using tflearn to load the csv file. However, the classes column of my data set has words such as iris-setosa, iris-versicolor, iris-virginica.
Nueral networks work only with numbers. So, I have to find a way to change the classes from words to numbers. Since it is a very small dataset, I can do it manually using Excel/text editor. I manually assigned numbers for different classes.
But, I can't possibly do it for every dataset I work with. So, I tried using pandas to perform one hot encoding.
preprocess_data = pd.read_csv("F:\Gautam\.....\Dataset\iris_data.csv")
preprocess_data = pd.get_dummies(preprocess_data)
But now, I can't use this piece of code:
data, labels = load_csv('filepath', categorical_labels=True,
n_classes=3)
'filepath' should only be a directory to the csv file, not any variable like preprocess_data.
Original Dataset:
Sepal Length Sepal Width Petal Length Petal Width Class
89 5.5 2.5 4.0 1.3 iris-versicolor
85 6.0 3.4 4.5 1.6 iris-versicolor
31 5.4 3.4 1.5 0.4 iris-setosa
52 6.9 3.1 4.9 1.5 iris-versicolor
111 6.4 2.7 5.3 1.9 iris-virginica
Manually modified dataset:
Sepal Length Sepal Width Petal Length Petal Width Class
89 5.5 2.5 4.0 1.3 1
85 6.0 3.4 4.5 1.6 1
31 5.4 3.4 1.5 0.4 0
52 6.9 3.1 4.9 1.5 1
111 6.4 2.7 5.3 1.9 2
Here's my code which runs perfectly, but, I have modified the dataset manually.
import numpy as np
import pandas as pd
import tflearn
from tflearn.layers.core import input_data, fully_connected
from tflearn.layers.estimator import regression
from tflearn.data_utils import load_csv
data_source = 'F:\Gautam\.....\Dataset\iris_data.csv'
data, labels = load_csv(data_source, categorical_labels=True,
n_classes=3)
network = input_data(shape=[None, 4], name='InputLayer')
network = fully_connected(network, 9, activation='sigmoid', name='Hidden_Layer_1')
network = fully_connected(network, 3, activation='softmax', name='Output_Layer')
network = regression(network, batch_size=1, optimizer='sgd', learning_rate=0.2)
model = tflearn.DNN(network)
model.fit(data, labels, show_metric=True, run_id='iris_dataset', validation_set=0.1, n_epoch=2000)
I want to know if there's any other built-in function in tflearn (or in any other module, for that matter) that I can use to modify the value of my classes from words to numbers. I don't think manually modifying the datasets would be productive.
I'm a beginner in tflearn and neural networks also. Any help would be appreciated. Thanks.
Use label encoder from sklearn library:
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
df = pd.read_csv('iris_data.csv',header=None)
df.columns=[Sepal Length,Sepal Width,Petal Length,Petal Width,Class]
enc=LabelEncoder()
df['Class']=enc.fit_transform(df['Class'])
print df.head(5)
if you want One-hot encoding then first you need to labelEncode then do OneHotEncoding :
enc=LabelEncoder()
enc_1=OneHotEncoder()
df['Class']=enc.fit_transform(df['Class'])
df['Class']=enc_1.fit_transform([df['Class']]).toarray()
print df.head(5)
These encoders first sort the words in alphabetical order then assign them labels. If you want to see which label is assigned to which class, do:
for k in list(enc.classes_) :
print 'name ::{}, label ::{}'.format(k,enc.transform([k]))
If you want to save this dataframe as a csv file, do:
df.to_csv('Processed_Irisdataset.csv',sep=',')
The simpliest solution is map by dict of all possible values:
df['Class'] = df['Class'].map({'iris-versicolor': 1, 'iris-setosa': 0, 'iris-virginica': 2})
print (df)
Sepal Length Sepal Width Petal Length Petal Width Class
0 89 5.5 2.5 4.0 1.3 1
1 85 6.0 3.4 4.5 1.6 1
2 31 5.4 3.4 1.5 0.4 0
3 52 6.9 3.1 4.9 1.5 1
4 111 6.4 2.7 5.3 1.9 2
If want generate dictionary by all unique values:
d = {v:k for k, v in enumerate(df['Class'].unique())}
print (d)
{'iris-versicolor': 0, 'iris-virginica': 2, 'iris-setosa': 1}
df['Class'] = df['Class'].map(d)
print (df)
Sepal Length Sepal Width Petal Length Petal Width Class
0 89 5.5 2.5 4.0 1.3 0
1 85 6.0 3.4 4.5 1.6 0
2 31 5.4 3.4 1.5 0.4 1
3 52 6.9 3.1 4.9 1.5 0
4 111 6.4 2.7 5.3 1.9 2