I am writing code for a Naive Bayes model(I know there's a standard implementation in Sklearn, but I want to code it anyway) - For this I have say upwards of 30 features, against all of which I have the corresponding click & impression counts (Treat them as True/False flags)
What I need then, is to calculate
P(Click/F1, F2.. F30) = (P(Click)*P(F1/Click)*P(F2|click) ..*P(F30|Click))/(P(F1, F2...F30), and
P(NoClick/F1, F2.. F30) = (P(NoClick)*P(F1/NoClick)*P(F2|Noclick) ..*P(F30|NOClick))/(P(F1, F2...F30)
Where I will disregard the denominator as it will affect both Click & Non click behaviour similarly.
Example, for two features, day_custom & is_tablet_phone, I have
is_tablet_phone click impression
FALSE 375417 28291280
TRUE 17743 4220980
day_custom click impression
Fri 77592 7029703
Mon 43576 3773571
Sat 65950 5447976
Sun 66460 5031271
Thu 74329 6971541
Tue 55282 4575114
Wed 51555 4737712
My approach to the Problem : Assuming I read the individual files in data frame, one after another, I want the abilty to calculate & store the corresponding Probablities back in a file, that I will then use for real time prediction of Probabilty to click vs no click.
One possible structure of "processed file" thus would be -:
Here's my entire code -:
In the full blown example, I am traversing the entire directory structure(of 30 txt files, one at a time, from the base path) - which is why I need the ability to create "names" at runtime.
for base_path in base_paths:
for root, dirs, files in os.walk(base_path):
for file in files:
file_paths.append(os.path.join(root, file))
For reasons of tractability, follow from here, by taking the 2 txt files as sample input
file_paths=['/home/ekta/Desktop/NB/day_custom.txt','/home/ekta/Desktop/NB/is_tablet_phone.txt']
flag=0
for filehandle in file_paths:
feature_name=filehandle.split("/")[-1].split(".")[0]
df= pd.read_csv(filehandle,skiprows=0, encoding='utf-8',sep='\t',index_col=False,dtype={feature_name: object,'click': int,'impression': int})
df2=df[(df.impression-df.click>0) & (df.click >0)]
if flag ==0:
MySumC,MySumNC,Mydict=0,0,collections.defaultdict(dict)
MySumC=sum(df2['click'])
MySumNC=sum(df2['impression'])
P_C=float(MySumC)/float(MySumC+MySumNC)
P_NC=1-P_C
for feature_value in df2[feature_name]:
Mydict[feature_name+'_'+feature_value]={'P_'+feature_name+'_'+feature_value+'_C':(df2[df2[feature_name]==feature_value]['click']*float(P_C))/MySumC, \
'P_'+feature_name+'_'+feature_value+'_NC':(df2[df2[feature_name]==feature_value]['impression']*float(P_NC))/MySumNC}
flag=1 %Set the flag as "1" because we don't need to compute the MySumC,MySumNC, P_C & P_NC again
Question :
It looks like THIS loop is the killer here.Also, intutively, looping on a dataframe is a BAD practice. How can I rewrite this, perhaps using Map/Apply ?
for feature_value in df2[feature_name]:
Mydict[feature_name+'_'+feature_value]={'P_'+feature_name+'_'+feature_value+'_C':(df2[df2[feature_name]==feature_value]['click']*float(P_C))/MySumC, \
'P_'+feature_name+'_'+feature_value+'_NC':(df2[df2[feature_name]==feature_value]['impression']*float(P_NC))/MySumNC}
What I need in Mydict , which is a hash to store each feature name and each feature value in it
{'day_custom_Mon':{'P_day_custom_Mon_C':.787,'P_day_custom_Mon_NC': 0.556},
'day_custom_Tue':{'P_day_custom_Tue_C':0.887,'P_day_custom_Tue_NC': 0.156},
'day_custom_Wed':{'P_day_custom_Tue_C':0.087,'P_day_custom_Tue_NC': 0.167}
'day_custom_Thu':{'P_day_custom_Tue_C':0.947,'P_day_custom_Tue_NC': 0.196},
'is_tablet_phone_True':{'P_is_tablet_phone_True_C':.787,'P_is_tablet_phone_True_NC': 0.066},
'is_tablet_phone_False':{'P_is_tablet_phone_False_C':.787,'P_is_tablet_phone_False_NC': 0.077},
.. and so on..
%PPS: I just made up those float numbers, but you get the point
Also because I will later serialize this file & pass to Redis directly, for other systems to feed on it, in an cron-job manner, so I need to preserve some sort of Dynamic naming .
What I tried -:
Since I am reading feature_name as
feature_name=filehandle.split("/")[-1].split(".")[0]` # thereby abstracting & creating variables dynamically
def funct1(row):
return row[feature_name]
def funct2(row):
return row['click']
def funct3(row):
return row['impression']
then..
df2.apply(funct2,axis=1)df2.apply(funct,axis=1)*float(P_C))/MySumC, df2.apply(funct3,axis=1)*float(P_NC))/MySumNC Gives me both the values I need for a feature_value(say Mon, Tue, Wed, and so on..) for a feature_name (say,day_custom)
I also know that df2.apply(funct1, axis=1) contains part of mycustom "names"(ie feature values), how would I then build these names using map/apply ?
Ie. I will have the values, but how would I create the "key" 'P_'+feature_name+'_'+feature_value+'_C' , since feature value post apply is returned as a series object.
check out the following recipe which does exactly what you want, only using data frame manipulations. I also simplified the actual frequency calculation a bit ;)
#set the feature name values as the index of
df2.set_index(feature_name, inplace=True)
#This is what df2.set_index() looks like:
# click impression
#day_custom
#Fri 9917 3163
#Mon 2566 3818
#Sat 8725 7753
#Sun 6938 8642
#Thu 6136 2556
#Tue 5234 2356
#Wed 9463 9433
#rename the index of your data frame
df2.rename(index=lambda x:"%s_%s"%('day_custom', x), inplace=True)
#compute the total sum of your data frame entries
totsum = float(df2.values.sum())
#use apply to multiply every data frame element by the total sum
df2 = df2.applymap(lambda x:x/totsum)
#transpose the data frame to have the following shape
#day_custom day_custom_Fri day_custom_Mon ...
#click 0.102019 0.037468 ...
#impression 0.087661 0.045886 ...
#
#
dftranspose = df2.T
# template kw for formatting
templatekw = {'click':"P_%s_C", 'impression':"P_%s_NC"}
# build a list of small data frames with correct index names P_%s_NC etc
dflist = [dftranspose[[col]].rename(lambda x:templatekw[x]%col) for col in dftranspose]
#use the concatenate function to produce a sparse dictionary
MyDict= pd.concat(dflist).to_dict()
Instead of assigning to MyDict at the end, you can use the update-method during the loop.
For understanding the comments below, see here my
Original answer:
Try to use a pivot_table:
def clickfunc(x):
return np.sum(x) * P_C / MySumC
def impressionfunc(x):
return np.sum(x) * P_NC / MySumNC
newtable = df2.pivot_table(['click', 'impression'], 'feature_name', \
aggfunc=[clickfunc, impressionfunc])
#transpose the table for the dictionary to have the right form
newtable = newtable.T
#to_dict functionality already gives the correct result
MyDict = newtable.to_dict()
#rename by copying
for feature_value, subdict in MyDict.items():
word = feature_name +"_"+ feature_value
copydict[word] = {'P_' + word + '_C':subdict['click'],\
'P_' + word + '_NC':subdict['impression'] }
This gives you the result you want in copydict
itertuples() is what worked for me(worked at lightspeed) - though It is still not using the map/apply approach that I so much wanted to see. Itertuples on a pandas dataframe returns the whole row, so I no longer have to do df2[df2[feature_name]==feature_value]['click'] - be aware that this matching by value is not only expensive, but also undesired, since it may return a series, if there were duplicate rows. itertuples solves that problem were elegantly, though I need to then access the individual objects/columns by integer indexes , which means less re-usable code. I could abstract this, but It wont be like accessing by column names, the status-quo.
for row in df2.itertuples():
Mydict[feature_name+'_'+str(row[1])]={'P_'+feature_name+'_'+str(row[1])+'_C':(row[2]*float(P_C))/MySumC, \
'P_'+feature_name+'_'+str(row[1])+'_NC':(row[3]*float(P_NC))/MySumNC}
Note that I am accesing each column in the row by row[1] , row[2] and like. For example, row has (0, u'Fri', 77592, 7029703)
Post this I get
dict(Mydict)
{'day_custom_Thu': {'P_day_custom_Thu_NC': 0.18345372640838162, 'P_day_custom_Thu_C': 0.0019559423132143377}, 'day_custom_Mon': {'P_day_custom_Mon_C': 0.0011466875948906617, 'P_day_custom_Mon_NC': 0.099300235316209587}, 'day_custom_Sat': {'P_day_custom_Sat_NC': 0.14336163246883712, 'P_day_custom_Sat_C': 0.0017354517827023852}, 'day_custom_Tue': {'P_day_custom_Tue_C': 0.001454726996987919, 'P_day_custom_Tue_NC': 0.1203925662982053}, 'day_custom_Sun': {'P_day_custom_Sun_NC': 0.13239618235343156, 'P_day_custom_Sun_C': 0.0017488722589598259}, 'is_tablet_phone_TRUE': {'P_is_tablet_phone_TRUE_NC': 0.11107365073163174, 'P_is_tablet_phone_TRUE_C': 0.00046690100046229593}, 'day_custom_Wed': {'P_day_custom_Wed_NC': 0.12467127727567069, 'P_day_custom_Wed_C': 0.0013566522616712882}, 'day_custom_Fri': {'P_day_custom_Fri_NC': 0.1849842396242351, 'P_day_custom_Fri_C': 0.0020418070466026303}, 'is_tablet_phone_FALSE': {'P_is_tablet_phone_FALSE_NC': 0.74447539516197614, 'P_is_tablet_phone_FALSE_C': 0.0098789704610580936}}
Related
I am trying to calculate a portfolio's daily total price, by multiplying weights of each asset with the daily price of the assets.
Currently I have a DataFrame tw which is all zeros except for the dates that I want to re-balance, which holds my assets weights. What I would like to do is for each month, populate the zeros with the weights I am trying to re-balance with, till the next re-balancing date, and so on and so forth.
My code:
df_of_weights = tw.loc[dates_to_rebalance[13]:]
temp_date = dates_to_rebalance[13]
counter = 0
for date in df_of_weights.index:
if date.year == temp_date.year and date.month == temp_date.month:
if date.day == temp_date.day:
pass
else:
df_of_weights.loc[date] = df_of_weights.loc[temp_date].values
counter += 1
temp_date = dates_to_rebalance[13+counter]
I understand that if you slice your DataFrame and assign it to a variable (df_of_weights), changing the values of said variable would not affect the original DataFrame. However, the values in tw changed. Have been searching for an answer online for a while now and am really confused.
You should use copy in order to fix the problem such that:
df_of_weights = tw.loc[dates_to_rebalance[13]:].copy()
The problem is slicing provides view instead of copy. The issue is still open.
https://github.com/pandas-dev/pandas/issues/15631
I'm trying to perform calculations based on the entries in a pandas dataframe. The dataframe looks something like this:
and it contains 1466 rows. I'll have to run similar calculations on other dfs with more rows later.
What I'm trying to do, is calculate something like mag='(U-V)/('R-I)' (but ignoring any values that are -999), put that in a new column, and then z_pred=10**((mag-c)m) in a new column (mag, c and m are just hard-coded variables). I have other columns I need to add too, but I figure that'll just be an extension of the same method.
I started out by trying
for i in range(1):
current = qso[:]
mag = (U-V)/(R-I)
name = current['NED']
z_pred = 10**((mag - c)/m)
z_meas = current['z']
but I got either a Series for z, which I couldn't operate on, or various type errors when I tried to print the values or write them to a file.
I found this question which gave me a start, but I can't see how to apply it to multiple calculations, as in my situation.
How can I achieve this?
Conditionally adding calculated columns row wise are usually performed with numpy's np.where;
df['mag'] = np.where(~df[['U', 'V', 'R', 'I']].eq(-999).any(1), (df.U - df.V) / (df.R - df.I), -999)
Note; assuming here that when any of the columns contain '-999' it will not be calculated and a '-999' is returned.
I have a data in the following form:
product/productId B000EVS4TY
1 product/title Arrowhead Mills Cookie Mix, Chocolate Chip, 1...
2 product/price unknown
3 review/userId A2SRVDDDOQ8QJL
4 review/profileName MJ23447
5 review/helpfulness 2/4
6 review/score 4.0
7 review/time 1206576000
8 review/summary Delicious cookie mix
9 review/text I thought it was funny that I bought this pro...
10 product/productId B0000DF3IX
11 product/title Paprika Hungarian Sweet
12 product/price unknown
13 review/userId A244MHL2UN2EYL
14 review/profileName P. J. Whiting "book cook"
15 review/helpfulness 0/0
16 review/score 5.0
17 review/time 1127088000
I want to convert it to a dataframe such that the entries in the 1st column
product/productId
product/title
product/price
review/userId
review/profileName
review/helpfulness
review/score
review/time
review/summary
review/text
are the column headers with the values arranged corresponding to each header in the table.
I still had a tiny doubt about your file, but since both my suggestions are quite similar, I will try to address both the scenarios you might have.
In case your file doesn't actually have the line numbers inside of it, this should do it:
filepath = "./untitled.txt" # you need to change this to your file path
column_separator="\s{3,}" # we'll use a regex, I explain some caveats of this below...
# engine='python' surpresses a warning by pandas
# header=None is that so all lines are considered 'data'
df = pd.read_csv(filepath, sep=column_separator, engine="python", header=None)
df = df.set_index(0) # this takes column '0' and uses it as the dataframe index
df = df.T # this makes the data look like you were asking (goes from multiple rows+1column to multiple columns+1 row)
df = df.reset_index(drop=True) # this is just so the first row starts at index '0' instead of '1'
# you could just do the last 3 lines with:
# df = df.set_index(0).T.reset_index(drop=True)
If you do have line numbers, then we just need to do some little adjustments
filepath = "./untitled1.txt"
column_separator="\s{3,}"
df = pd.read_csv(filepath, sep=column_separator, engine="python", header=None, index_col=0)
df.set_index(1).T.reset_index(drop=True) #I did all the 3 steps in 1 line, for brevity
In this last case, I would advise you change it in order to have line numbers in all of them (in the example you provided, the numbering starts at the second line, this might be an option about how you handle headers when exporting the data in whatever tool you might be using
Regarding the regex, the caveat is that "\s{3,}" looks for any block of 3 consecutive whitespaces or more to determine the column separator. The problem here is that we'll depend a bit on the data to find the columns. For instance, if in any of the values just so happens to appear 3 consecutive spaces, pandas will raise an exception, since the line will have one more column than the others. One solution to this could be increasing it to any other 'appropriate' number, but then we still depend on the data (for instance, with more than 3, in your example, "review/text" would have enough spaces for the two columns to be identified)
edit after realising what you meant by "stacked"
Whatever "line-number scenario" you have, you'll need to make sure you always have the same number of columns for all registers and reshape the continuous dataframe with something similar to this:
number_of_columns = 10 # you'll need to make sure all "registers" do have the same number of columns otherwise this will break
new_shape = (-1,number_of_columns) # this tuple will mean "whatever number of lines", by 10 columns
final_df = pd.DataFrame(data = df.values.reshape(new_shape)
,columns=df.columns.tolist()[:-10])
Again, take notice of making sure that all lines have the same number of columns (for instance, a file with just the data you provided, assuming 10 columns, wouldn't work). Also, this solution assumes all columns will have the same name.
I'm very confused on the apply function for pandas. I have a big dataframe where one column is a column of strings. I'm then using a function to count part-of-speech occurrences. I'm just not sure the way of setting up my apply statement or my function.
def noun_count(row):
x = tagger(df['string'][row].split())
# array flattening and filtering out all but nouns, then summing them
return num
So basically I have a function similar to the above where I use a POS tagger on a column that outputs a single number (number of nouns). I may possibly rewrite it to output multiple numbers for different parts of speech, but I can't wrap my head around apply.
I'm pretty sure I don't really have either part arranged correctly. For instance, I can run noun_count[row] and get the correct value for any index but I can't figure out how to make it work with apply how I have it set up. Basically I don't know how to pass the row value to the function within the apply statement.
df['num_nouns'] = df.apply(noun_count(??),1)
Sorry this question is all over the place. So what can I do to get a simple result like
string num_nouns
0 'cat' 1
1 'two cats' 1
EDIT:
So I've managed to get something working by using list comprehension (someone posted an answer, but they've deleted it).
df['string'].apply(lambda row: noun_count(row),1)
which required an adjustment to my function:
def tagger_nouns(x):
list_of_lists = st.tag(x.split())
flat = [y for z in list_of_lists for y in z]
Parts_of_speech = [row[1] for row in flattened]
c = Counter(Parts_of_speech)
nouns = c['NN']+c['NNS']+c['NNP']+c['NNPS']
return nouns
I'm using the Stanford tagger, but I have a big problem with computation time, and I'm using the left 3 words model. I'm noticing that it's calling the .jar file again and again (java keeps opening and closing in the task manager) and maybe that's unavoidable, but it's really taking far too long to run. Any way I can speed it up?
I don't know what 'tagger' is but here's a simple example with a word count that ought to work more or less the same way:
f = lambda x: len(x.split())
df['num_words'] = df['string'].apply(f)
string num_words
0 'cat' 1
1 'two cats' 2
There is an inconsistency with dataframes that I cant explain. In the following, I'm not looking for a workaround (already found one) but an explanation of what is going on under the hood and how it explains the output.
One of my colleagues which I talked into using python and pandas, has a dataframe "data" with 12,000 rows.
"data" has a column "length" that contains numbers from 0 to 20. she wants to divided the dateframe into groups by length range: 0 to 9 in group 1, 9 to 14 in group 2, 15 and more in group 3. her solution was to add another column, "group", and fill it with the appropriate values. she wrote the following code:
data['group'] = np.nan
mask = data['length'] < 10;
data['group'][mask] = 1;
mask2 = (data['length'] > 9) & (data['phraseLength'] < 15);
data['group'][mask2] = 2;
mask3 = data['length'] > 14;
data['group'][mask3] = 3;
This code is not good, of course. the reason it is not good is because you dont know in run time whether data['group'][mask3], for example, will be a view and thus actually change the dataframe, or it will be a copy and thus the dataframe would remain unchanged. It took me quit sometime to explain it to her, since she argued correctly that she is doing an assignment, not a selection, so the operation should always return a view.
But that was not the strange part. the part the even I couldn't understand is this:
After performing this set of operation, we verified that the assignment took place in two different ways:
By typing data in the console and examining the dataframe summary. It told us we had a few thousand of null values. The number of null values was the same as the size of mask3 so we assumed the last assignment was made on a copy and not on a view.
By typing data.group.value_counts(). That returned 3 values: 1,2 and 3 (surprise) we then typed data.group.value_counts.sum() and it summed up to 12,000!
So by method 2, the group column contained no null values and all the values we wanted it to have. But by method 1 - it didnt!
Can anyone explain this?
see docs here.
You dont' want to set values this way for exactly the reason you pointed; since you don't know if its a view, you don't know that you are actually changing the data. 0.13 will raise/warn that you are attempting to do this, but easiest/best to just access like:
data.loc[mask3,'group'] = 3
which will guarantee you inplace setitem